Data Scientist (Gen/Agentic AI solutions)

Job Title: Data Scientist (Gen/Agentic AI solutions) Company: Holcim Job Category: Machine Learning Roles & Responsibilities: About Holcim As the world’s global leader in innovative and sustainable building materials, Holcim is reinventing the way the world builds. Supported by a 45,000\-strong global team spread across 44 countries and four industry segments (Cement, Aggregates, Ready\-Mix Concrete and Solutions \& Products), we are committed to shaping a greener, smarter and healthier world. It’s our ambition to lead the industry in reducing carbon emissions and accelerating the transition towards low\-carbon constructions globally. Experience: * 8\+ years of progressive experience in data science and machine learning, with a minimum of 3 years focused on Generative AI and LLM\-based systems. * Demonstrated track record of delivering AI solutions at enterprise scale * Hands\-on experience with full AI/ML lifecycle management: data engineering, feature stores, model training, evaluation, deployment, and monitoring. * Industry experience especially in Manufacturing Function in a Building Material Industry, Manufacturing, Process or Pharma is preferred. Key Responsibility: * Platform Prototyping: Design and implement core ML components, such as feature stores, model registries, and automated evaluation pipelines. * Standardization: Establish best practices for the ML lifecycle, from data labeling and experimentation to CI/CD for ML (MLOps). * Scalability: Optimize model inference and training workflows to handle high\-throughput, low\-latency requirements. * Internal Consulting: Act as a subject matter expert for product\-facing data science teams, helping them leverage platform tools to solve complex business problems. * Tooling \& Automation: Build internal libraries and SDKs that simplify the transition from a local research environment to a distributed production environment. * RAG Infrastructure: Design and optimize high\-performance retrieval systems using vector databases (e.g., Pinecone, Weaviate) and advanced semantic search techniques. * LLM Evaluation Frameworks: Build automated "vibe\-check" replacements. Develop rigorous evaluation pipelines using LLM\-as\-a\-judge, G\-Eval, or custom scoring rubrics to measure hallucination, faithfulness, and relevancy. * Agentic Orchestration: Develop and standardize the use of agentic frameworks (e.g., LangGraph, CrewAI) to allow product teams to build complex, multi\-step AI workflows. * Model Lifecycle Management: Manage the transition between model providers (OpenAI, Anthropic, Google) and open\-source alternatives (Llama 3\+, Mistral) through unified abstraction layers. * Cost \& Latency Optimization: Implement caching strategies (e.g., GPTCache), prompt compression, and token\-usage monitoring to ensure the platform remains economically viable. * Guardrails \& Safety: Integrate real\-time content filtering and PII masking to ensure all LLM outputs comply with corporate security and ethical standards. Work Location: Navi Mumbai, Maharashtra, India Availability: Full time

About The Role Qualifications: * BE / B. Tech in Computer Science, Engineering or relevant field * Graduate degree in Data Science or other quantitative field is preferred * Strong mathematics skills (e.g. statistics, algebra) * Certification in Gen/Agentic AI solutions * Certification in Platforms – Databricks,AWS is preferred Required skills: * Proficiency in Python; strong working knowledge of relevant libraries \& frameworks: LangChain, LangGraph, HuggingFace Transformers, PyTorch, scikit\-learn, Pandas, and NumPy. * Deep experience with LLM APIs (OpenAI, Anthropic, Google, Mistral) and open\-source model deployment via Ollama, vLLM, or TGI. * Solid command of vector databases, semantic search, and knowledge graph technologies for enterprise RAG architectures. * Proficiency with MLOps tooling: MLflow, Weights \& Biases, Kubeflow, or similar; experience with LLMOps tools such as LangSmith. * Strong SQL and experience with modern data platforms such as Databricks for AI\-ready data preparation. * Understanding of software engineering best practices: version control (Git), containerization (Docker/Kubernetes), API design (REST/gRPC), and CI/CD pipelines. * Knowledge of how to benchmark GenAI models beyond simple accuracy (e.g., toxicity, bias, and reasoning depth). * Exposure to multi\-modal AI systems incorporating vision, audio, or structured document understanding (PDFs, tables, charts). * Good understanding of the GENAI standards (MCP, A2A, A2UI etc.) Key Skills: Machine Learning, Python, PyTorch, Pandas, NumPy, Model Training Salary: Not disclosed