Job Title: T&T-EAID-Senior Consultant-ETL Testing-Delhi-Engineering-Quality Engineering Company: Deloitte Job Category: Machine Learning Roles & Responsibilities: Deloitte’s Technology \& Transformation: EAD: Engineering practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact\-driven decision\-making and generate an insight\-driven advantage. Our practice addresses the continuum of opportunities in business intelligence \& visualization, data management, performance management and next\-generation analytics and technologies, including big data, cloud, cognitive and machine learning. * Data Testing: + Strong SQL (Big Query preferred) + DBT and automated testing frameworks * Report Testing: + Power BI dashboard and DAX testing * DevOps \& CI/CD: + CI/CD pipelines and Git workflows * Security \& Governance: + IAM and data governance awareness * Delivery Approach: + Agile delivery + JIRA and Confluence + Collaboration across teams * Specialisations: + Financial services domain experience + SQL\-based testing and Python scripting + Data warehouse testing * 5\+ years’ experience * Strong problem solving * Stakeholder management * Public IBOR/ABOR knowledge or understanding of Aladdin data (will support the Public IBOR team) * Aladdin platform (Asset, Liability, Debt and Derivative Investment Network) Work Location: Gurgaon, India Availability: Full time

Job Title: Senior Data Science Engineer - Mobility Company: Eaton Job Category: Machine Learning Roles & Responsibilities: "The Centre for Intelligent Power located at Eaton India’s Innovation Centre in Pune and Eaton’s global headquarters in Dublin, applies data science to transform all aspects of our company. We’re working on solving the technical challenges of today’s digital world, while developing ground\-breaking technology that’s going to power the next generation of futuristic products and services. We’re continuing to expand our organization and we are excited to announce that we are recruiting a Senior Data Scientist to join our team. The Senior Data Scientist will majorly collaborate with Eaton's Mobility product group to develop new data\-science based product features. The features will primarily address emergent use cases in the mobility and automotive domains. The successful candidate will be expected to analyse requirements to drive the research and development of Machine Learning techniques to production of the features and monitoring after. In addition, the candidate will be expected to understand and consider system constraints – especially constraints related to implementing algorithms on resource\-constrained platforms. This position will be part of the planned Mobility Spin\-off." * Collaborate with Eaton's mobility business units to identify and evaluate opportunities for leveraging data science, particularly for enhancing future product features. * Lead the research and development of problem statements on complex datasets using cutting\-edge data science techniques, including traditional machine learning, deep learning, and generative AI, while proving feasibility. * Design and develop algorithms and datasets while accounting for system constraints and ensuring alignment with platform requirements. * Plan and manage project roadmaps, estimate timelines and resources, and proactively address blockers or technical challenges throughout the project lifecycle. * Document and communicate solutions effectively through flowcharts, diagrams, code comments, snippets, and performance metrics. * Ability to lead a project independently without handholding and able to break down a project into technical requirements and work on solution design as well. * Ability to work with and collaborate with Data Engineers, DevOps, MLOps, QA and UI/UX teams to execute and deliver projects * Mentor and guide team members in the use of data science tools, techniques, and best practices. * Stay updated on the latest advancements in data science methodologies, tools, and technologies. * Safeguard intellectual property and ensure compliance with relevant policies. * Contribute towards filing patents, IP creation, disclosures and white paper publications" * Extensive practical experience across multiple projects of developing algorithms involving state\-of\-the\-art AI/ML techniques, including deep learning * Skilled in the disciplined application of data science to solve business problems \- time\-boxed exploratory research, track progress with respect to defined metrics, frequent stakeholder feedback * Excellent Python programmer and following standard coding practices * Exposure to LLMs, SLMs and Generative AI * Exposure of MATLAB/Simulink programming * Experience working with Agile development methodologies and concepts. * Excellent verbal and written communication skills * Strong stakeholder management experience with the ability to influence key decision makers. * High comfort engaging with academic literature. * Research oriented mindset with ability to do extensive literature search for solving any problem Work Location: Pune Division, Maharashtra, India Availability: Full time

Job Title: Lead Data Scientist Company: Philips Job Category: Machine Learning Roles & Responsibilities: Lead Data Scientist Job Description Job title: Lead Data Scientist Your Role The Lead Data Scientist architects, builds, and runs production\-grade Machine Learning and Generative AI systems—owning the full lifecycle from model development to scalable cloud deployment and ongoing performance monitoring . In addition, the role partners with commercial stakeholders translate market/customer data into decision\-ready insights and AI\-enabled analytics solutions that drive measurable outcomes Operating with a builder and translator mindset , the individual rapidly develops MVP analytics solutions , leverages AI to accelerate insight generation , and ensures strong product engineering fundamentals, data quality , and governance . The role plays a critical part in establishing a single source of truth for performance management across markets and channels while elevating analytics maturity from descriptive reporting to predictive and insight\-led decision making . Key Responsibilities * ML \& Deep Learning Model Development * Design, train, and optimize ML models for prediction, classification, ranking, time\-series forecasting, anomaly detection, NLP, and recommendation use cases. * Build robust experimentation workflows (train/validation strategy, ablations, error analysis) and improve model quality through iterative tuning. * Ensure reproducibility and maintainability through clean code practices, versioning, and automated testing. * GenAI Engineering (LLMs, RAG / MCP / fine\-tuning, Agents) * Build enterprise\-grade LLM applications using RAG (retrieval\-augmented generation), MCP, and fine\-tuning approaches: chunking strategies, embedding generation, hybrid retrieval, reranking, prompt templates, and citation/attribution patterns. * Develop LLM applications with tool use/function calling patterns and agentic workflows where appropriate. * Implement systematic evaluation: curated eval sets, prompt regression tests, hallucination checks, retrieval quality metrics, and automated quality gates. * ML \& LLM Operations: Productionization, Deployment \& Monitoring * Deploy and operate real\-time and batch inference solutions on Azure using managed endpoints and/or containerized serving. * Build CI/CD for ML systems: automated packaging, container builds, model validation tests, staged rollouts, and rollback strategies. * Establish lifecycle management: model registry/versioning, lineage, promotion workflows, and release governance. * Implement observability: latency, throughput, cost, drift signals, data quality checks, alerts, and performance degradation monitoring. * Pipeline Orchestration \& Automation (Train → Deploy) * Build standardized ML pipelines for training, evaluation, and deployment using orchestration tools (cloud\-native pipelines and/or platform tools). * Automate dataset/version management, feature generation, scheduled retraining triggers, and approval workflows. * Define repeatable patterns for scalable experimentation and reliable production delivery. * Analytics Products, Dashboards \& Data Governance * Own key analytics outputs as products (dashboards, reusable datasets, internal tools), continuously improving them based on usage patterns and performance gaps. * Build and automate dashboards and analytical components using scalable SQL logic, Python transformations, and reusable modules. * Act as owner for critical commercial/syndicated datasets (e.g., GfK, Circana, Nielsen or equivalent): definitions, assumptions, and limitations, ensuring transparent logic and trust in outputs. * Partner with data engineering/IT to ensure data quality, harmonization, and governance through strong validation and reconciliation practices. * Stakeholder Partnership \& Decision Support (Lightweight, High Impact) * Serve as trusted analytics thought partner to senior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shaping problem statements and aligning on success metrics. * Translate complex analytics into clear recommendations with a decision\-oriented storyline (“so\-what / now\-what”), tailored for leadership forums and reviews. * Support performance reviews, planning cycles, and high\-priority ad\-hoc requests with speed, rigor, and confidence; proactively challenge assumptions with fact\-based insights. * Responsible AI, Security, and Risk Controls (GenAI\-ready) * Implement guardrails: prompt injection defenses, sensitive data protections, output validation, and secure tool execution patterns. * Apply responsible AI practices: transparent evaluation criteria, auditability, and risk controls aligned to enterprise needs. * Technical Leadership (Lead\-level Expectations) * Set engineering standards for DS/ML codebases: design docs, code review practices, testing discipline, and production readiness checklists. * Mentor data scientists/ML engineers on modeling, GenAI engineering, and MLOps best practices. * Lead architectural decisions across modeling approaches, retrieval stack, serving patterns, and evaluation strategy. Core Skills \& Competencies Must\-have (Technical) * Strong Python (production\-quality coding) and solid CS fundamentals; strong SQL for data access and validation. * Depth in ML: Traditional ML exposure and at least one deep learning framework (PyTorch/TensorFlow), with strong understanding of metrics and failure modes. * GenAI implementation: RAG / MCP / fine\-tuning, embeddings/vector search, prompt orchestration, evaluation harnesses, and LLM application patterns. * Production deployment experience on AWS or Azure (model/LLM app deployment, API serving, scaling, monitoring). * MLOps tooling: experiment tracking, model registry, CI/CD, and pipeline orchestration (e.g., MLflow or equivalent patterns). Good\-to\-have (Business \+ Influence) * Strong business acumen and ability to connect disparate data points into compelling narratives that influence senior stakeholders. * Builder/MVP mindset—rapid prototyping and iterating based on stakeholder feedback while maintaining data quality and governance Note: This role values applied problem\-solving and business impact over purely academic specialization. You're The Right Fit If * Proven track record of owning end\-to\-end analytics domains, not just contributing to isolated analyses or consuming pre\-built reports. * 7–12\+ years in hands\-on Data Science / ML Engineering with multiple production deployments owned end\-to\-end. * Demonstrated ability to take solutions from experimentation → production (reproducible pipelines, deployment to managed endpoints/container platforms, monitoring \+ iterative improvement). * Strong GenAI delivery record: shipped RAG/MCP/fine\-tuned LLM applications with measurable quality controls, safety measures, and operational readiness. * Experience operating in complex, matrixed environments and partnering with senior stakeholders to drive insight\-led decision making * Hands\-on exposure to AI\-enabled analytics, including the use of GenAI tools (e.g., ChatGPT, Claude, or similar) to accelerate insight generation, analysis, or productivity. * Strong experience partnering with senior business stakeholders (BU leaders, Sales, Marketing, Finance), influencing decisions through insight\-led storytelling. Onsite roles require full\-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations. this role is an office role. Work Location: Bengaluru, Karnataka, India Availability: Full time

Job Title: Lead Machine Learning Engineer Company: Bain & Company Job Category: Machine Learning Roles & Responsibilities: You’ll join our Machine Learning and Data Science experts within the \[2] Applied Tech \& AI team. This team is part of Bain’s digital capabilities practice, which includes experts in analytics, engineering, product management, and design. In this multidisciplinary environment, you'll leverage deep technical expertise with business acumen to help clients tackle their most transformative challenges. You’ll work on integrated teams alongside our general consultants and clients to develop data\-driven strategies and innovative solutions. Together, we create human\-centric solutions that harness the power of data and artificial intelligence to drive competitive advantage for our clients. Our collaborative and supportive work environment fosters creativity and continuous learning, enabling us to consistently deliver exceptional results. * Design, build, and deliver statistical/ML models and agentic AI/LLM solutions to solve high\-impact client problems. * Architect and implement production\-grade ML systems by building enterprise\-grade services, applying MLOps and CI/CD for reliable deployment and monitoring, and ensuring high standards in engineering quality and documentation. * Partner with general consulting teams and client stakeholders to translate business problems into ML solutions, prioritize work, and enable enterprise‑scale AI transformation. * Continuously research emerging frameworks and state‑of‑the‑art AI (e.g., LLMs, agents, retrieval, post\-training, etc.) and convert learnings into reusable frameworks, components, and accelerators used across industries and functions. * Share knowledge and mentor others on best practices tools, and approaches in applied machine learning and AI engineering. * Contribute to internal R\&D and learning initiatives to advance Bain’s AI capabilities and foster continuous innovation between projects. Work Location: Karnataka, India Availability: Full time

Job Title: T&T | Engineering | ADMM | Consultant | Azure Databricks | Bengaluru Company: Deloitte Job Category: Machine Learning Roles & Responsibilities: Deloitte’s Technology \& Transformation practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact\-driven decision\-making and generate an insight\-driven advantage. Our practice addresses the continuum of opportunities in business intelligence \& visualization, data management, performance management and next\-generation analytics and technologies, including big data, cloud, cognitive and machine learning. We are seeking highly skilled Azure Databricks Data Engineers to join our data modernization team. You will play a pivotal role in designing, developing, and maintaining robust data solutions on the Databricks platform. Your experience in data engineering, along with a deep understanding of technology and tools, will be instrumental in building solutions to drive data\-driven decision\-making across a variety of customers. * Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric). * Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 \& Azure Blob storage). * Experience in utilizing SQL DML to query modern RDBMS in an efficient manner (e.g., SQL Server, PostgreSQL). * Good Hands\-on in PowerApps, Power Automate, Power BI. * Knowledge of REST\-API, OAuth. * Collaborate with data architects and other stakeholders to understand data requirements and deliver high\-quality data solutions. * Optimize data pipelines in the Azure environment for performance, scalability, and reliability. * Collect, clean, and validate data from multiple sources to ensure accuracy and reliability. * Ensure data quality and integrity through data validation techniques and frameworks. * Monitor and troubleshoot data pipeline issues to ensure timely resolution. * Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting\-edge. * Manage the CI/CD process for deploying and maintaining data solutions. * Good to have knowledge of any programming language like C\#, Java, JavaScript, Python etc. * Apply data cleansing, transformation, validation, and quality checks. * Ensure data accuracy, consistency, replication, and governance across platforms. * Strong knowledge of Microsoft Azure services, including Azure Data Factory, Azure Synapse, Azure Databricks, Azure Blob Storage and Azure Data Lake Gen 2\. * Experience utilizing SQL DML to query modern RDBMS in an efficient manner (e.g., SQL Server, PostgreSQL). * Strong understanding of Software Engineering principles and how they apply to Data Engineering (e.g., CI/CD, version control, testing). * Experience with Data Analysis tools. * Experience efficiently querying API endpoints as a data source. * Understanding of the Azure environment and related services such as subscriptions, resource groups, etc. * Understanding of Git workflows in software development. * Using Azure DevOps pipeline and repositories to deploy and maintain solutions. * Understanding of Ansible and how to use it in Azure DevOps pipelines. * 3\-5 years of proven experience as a Data Engineer or similar role dealing with data and ETL processes. * Experience working in regulated environments and with internal systems quality policies and procedures. * Experience in development and deployment of cloud infrastructure. * Strong SQL and Python skills for data transformation and analysis. * Design and implement data models, schemas, and performance\-optimized queries. * Ensure data quality, governance, and security in all solutions. * Deploy data solutions using Azure DevOps CI/CD pipelines. Work Location: Bengaluru, India Availability: Full time

Job Title: Senior Machine Learning Engineer Company: Talent et au-dela Job Category: Machine Learning Roles & Responsibilities: As a Senior Machine Learning Engineer, AI \& ML — Data Collection , you will play a critical role in building and scaling the company’s Unified AI/ML Data Collection Platform, enabling standardized, reliable, and scalable machine learning capabilities across the organization. This role will focus on transforming existing AI/ML and LLM\-driven data systems into a cohesive platform that supports data pipelines, model lifecycle management, evaluation frameworks, and production deployment. This position requires deep hands\-on expertise in machine learning engineering, LLM\-based systems, ML platform development, and MLOps. You will work closely with ML engineers, product managers, researchers, and business stakeholders to deliver production\-ready AI/ML systems aligned with broader business objectives and AI/ML strategy. You will be deeply involved in the design, development, and operationalization of platform components, including data ingestion, feature management, model training and evaluation, scalable inference systems, and model observability capabilities. You will help ensure that AI/ML systems are production\-ready, observable, maintainable, and cost\-efficient, with a strong emphasis on reliability, performance, governance, and developer productivity. You will leverage your expertise in areas such as large language models (LLM), retrieval\-augmented generation (RAG), embeddings, vector databases, distributed systems, cloud\-native architectures, and ML Operations (MLOps). You will contribute to the end\-to\-end lifecycle of ML systems, from experimentation and prototyping to deployment, monitoring, optimization, and continuous improvement while mentoring engineers and promoting strong engineering practices across the team. You will be part of a multidisciplinary team of ML engineers responsible for building and maintaining the Unified AI/ML Data Collection Platform. The team focuses on developing scalable systems that support data pipelines, model lifecycle management, LLM\-based workflows, and evaluation frameworks, enabling downstream teams to build and deploy AI\-driven data collection solutions. Outline of Duties and Responsibilities * AI\-Powered Data Collection Systems: Design and develop scalable AI\-driven data collection and enrichment workflows across structured and unstructured data sources. * LLM \& Generative AI Workflows: Build LLM\-based capabilities including RAG systems, prompt orchestration, entity extraction, summarization, classification, and automated validation workflows. * Agentic Frameworks \& Model Context Integration: Design and implement agentic workflows and model\-to\-tool integrations that connect AI models with internal tools, APIs, knowledge stores, data sources, and workflow systems. * Model Deployment \& Lifecycle Management: Deploy, maintain, and optimize ML and LLM models in production, including model versioning, CI/CD, experiment tracking, model registry, rollout strategies, and rollback mechanisms. * Data Quality \& Evaluation: Build frameworks for evaluating extraction quality, model performance, hallucination risks, grounding, consistency, latency, coverage, and overall data reliability. * Observability \& Operational Excellence: Implement monitoring, logging, tracing, alerting, cost tracking, model performance monitoring, drift detection, and reliability dashboards for production AI/ML systems. * Scalable Platform Engineering: Design distributed, event\-driven, and cloud\-native systems using asynchronous processing, message queues, containerization, and orchestration patterns to support high\-volume workloads. * Innovation \& Continuous Improvement: Evaluate emerging AI/ML technologies, LLM frameworks, orchestration tools, vector databases, and model deployment approaches to improve automation capabilities and developer productivity. * Company Values: Model company values and contribute to a culture of innovation, accountability, collaboration, inclusion, and continuous improvement. Work Location: Navi Mumbai, Maharashtra, India Availability: Full time

Job Title: Data Scientist 2 Company: Epsilon Job Category: Machine Learning Roles & Responsibilities: Overview About Business Unit: The Automotive Practice at Epsilon is a rapidly growing team, driving growth for major players in the automotive industry \- from Original Equipment Manufacturers (OEMs) to dealerships across North America. Part of a 1,600\-member multinational team, the practice provides the automotive world’s largest service reminder platform, alongside agency services and digital media solutions. A leader in the automotive space, the team supports over 50% of auto dealerships in North America and maintains relationships with over 280 million customers. Home to innovation and ground breaking technology, our Auto team leads the game in developing outstanding software and solutions for hyper\-personalized digital marketing. Role Overview The Automotive Data Science team within Epsilon partners both internal and external clients and data providers, leverage predictive analytics and advanced modeling techniques to drive strategic thought and effective decision making. The core work charter includes supporting Retail and After Sales Service businesses for all the OEMs by developing solutions across the Customer Journey to drive Acquisition, Engagement, Retention using Advanced Analytics, Machine Learning, Deep Learning Capabilities and Data Platforms. The Data Scientist, II would support stakeholders by deploying projects from inception to insights and enable business strategy and impact for the clients. Contributes towards automation, process improvement and efficiency. Demonstrates innovative thinking, passion, and cross team collaboration. Establish strong working relationship with partners. Responsibilities * Apply AI, machine learning, and deep learning techniques including regression, classification, clustering, tree\-based algorithms, support vector machines, neural networks, recommender systems, and marketing attribution models to solve complex business problems. * Design and implement end\-to\-end MLOps pipelines covering data ingestion, training, validation, deployment, and monitoring. * Support the deployment, monitoring, and maintenance of machine learning models in production, tracking model performance, data quality, scalability, and model drift to ensure reliable and sustained business value. * Enhance existing analytical methodologies by identifying new data sources, engineering relevant features, experimenting with alternative algorithms, fine\-tuning hyperparameters, and improving model performance and business outcomes. * Define analytical and technical requirements for developing scalable data science capabilities, machine learning platforms, data pipelines, and reusable analytical solutions. * Translate complex analytical findings and model outputs into actionable business recommendations, presenting insights effectively to partners * Automate data science and machine learning workflows to improve operational efficiency, reproducibility, and scalability while collaborating with cross\-functional teams to deliver production\-ready solutions. Additional Information Epsilon is a global data, technology and services company that powers the marketing and advertising ecosystem. For decades, we’ve provided marketers from the world’s leading brands the data, technology and services they need to engage consumers with 1 View, 1 Vision and 1 Voice. 1 View of their universe of potential buyers. 1 Vision for engaging each individual. And 1 Voice to harmonize engagement across paid, owned and earned channels. Epsilon’s comprehensive portfolio of capabilities across our suite of digital media, messaging and loyalty solutions bridge the divide between marketing and advertising technology. We process 400\+ billion consumer actions each day using advanced AI and hold many patents of proprietary technology, including real\-time modeling languages and consumer privacy advancements. Thanks to the work of every employee, Epsilon has been consistently recognized as industry\-leading by Forrester, Adweek and the MRC. Epsilon is a global company with more than 9,000 employees around the world. * People centricity: We focus on employee well\-being in an environment where colleagues truly care about each other. * Collaboration: We work together, support one another, and collectively achieve goals. * Growth: There are endless opportunities for growth through learning, development and career advancement. * Innovation: We drive progress through cutting\-edge solutions and forward\-thinking approaches. * Flexibility: We’ve created a balance between work and personal life, and we encourage adaptability to solve problems creatively. Work Location: Bengaluru, Karnataka, India Availability: Full time

Job Title: Applied AI and Data Science Specialist - Global Research Modernization Company: JPMorganChase Job Category: Machine Learning Roles & Responsibilities: Job Description Join J.P. Morgan's Global Research Center and help modernize research through applied AI, machine learning and data science, delivering innovative solutions that enhance research methodologies, workflows and outputs. As an Applied AI and Data Science Specialist within the Global Research Modernization team , you will be responsible for developing and implementing AI and data science solutions that enhance research methodologies and outputs. You will translate business problems into deployable data science solutions using Python, machine learning and Large Language Models (LLMs). You will collaborate with research analysts and stakeholders to identify opportunities for data\-driven insights and workflow automation. You will support solutions from data exploration and model development through validation and deployment. You will ensure solutions adhere to appropriate data integrity, regulatory and model governance standards. Job Responsibilities * Develop applied data science solutions using Python, from problem framing and data exploration through model development, validation and deployment. * Implement AI and data science strategies to enhance research methodologies and outputs. * Automate workflows for global research teams using AI techniques, including Large Language Models (LLMs). * Collaborate with research analysts and stakeholders to identify opportunities for data\-driven insights and automation. * Ensure the integrity, accuracy and accessibility of data used for analysis and model development. * Evaluate emerging AI and data science technologies for potential application within research. * Communicate analytical concepts, model outputs and insights effectively to stakeholders. * Adhere to regulatory, ethical, data\-use and model governance standards. ABOUT US JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. About The Team J.P. Morgan's Commercial \& Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial \& Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. Work Location: Mumbai, Maharashtra, India Availability: Full time

Job Title: AI Data Specialist / Data Annotator Company: Jyoti HR Recruiter , Talent Acquisition , Hiring for IT & Non IT Job Category: Machine Learning Roles & Responsibilities: Job Description: AI Data Specialist / Data Annotator 3Wheels.AI focuses on leveraging artificial intelligence, technology, and automation to develop innovative solutions. We are looking for a detail\-oriented and analytical AI Data Specialist / Data Annotator to help prepare, label, validate, and organize high\-quality datasets for AI and machine learning applications. Key Responsibilities * Annotate and label text, images, documents, and other datasets according to project guidelines. * Review and validate annotated data to ensure accuracy and consistency. * Categorize, classify, and organize datasets for AI and machine learning models. * Identify incorrect, incomplete, duplicate, or inconsistent data and correct it as required. * Use AI tools and prompt engineering to support data preparation and annotation tasks. * Perform data cleaning and quality checks using MS Excel and other relevant tools. * Follow annotation guidelines, quality standards, and project\-specific instructions. * Maintain clear documentation of completed tasks and report recurring data issues. * Collaborate with AI, data, and technical teams to improve data quality. * Handle confidential information responsibly and follow data privacy requirements. Work Location: In person Work Location: Lucknow, India Availability: Full time

Job Title: Senior AI Engineer Company: Enormous Enterprise LLC Job Category: Machine Learning Roles & Responsibilities: Key Responsibilities * Develop, train, evaluate, fine\-tune, and deploy Machine Learning and Deep Learning models. * Apply classification, regression, clustering, feature engineering, model selection, hyperparameter tuning, and model evaluation techniques. * Build AI applications using Python, Scikit\-learn, PyTorch, and/or TensorFlow. * Work with LLM architectures, embeddings, prompt engineering, context management, and LLM evaluation. * Develop RAG pipelines involving document processing, chunking, embeddings, retrieval, reranking, and vector databases. * Design and implement Agentic AI systems, including stateful workflows, multi\-step reasoning, tool calling, planning, routing, and orchestration. * Use LangGraph for agentic workflows and integrate AI agents with APIs, enterprise systems, databases, and external tools. * Apply the Model Context Protocol (MCP) in AI\-agent and tool\-integration workflows. * Build scalable AI services using REST APIs, FastAPI, microservices, and sound software engineering practices. * Support production deployment, monitoring, model versioning, and basic MLOps/LLMOps. * Work with Snowflake for data engineering, analytics, and AI workloads. * Follow responsible AI practices relating to privacy, security, compliance, bias, explainability, and model risk. Preferred Skills * LangGraph and multi\-agent orchestration. * Production\-grade GenAI application development. * Cloud platforms, particularly AWS. * Model deployment, monitoring, and LLMOps. * AI governance, responsible AI, privacy, security, and compliance. Work Location: Bengaluru, Karnataka, India Availability: Full time