AI Forward Deployed Engineer

Job Title: AI Forward Deployed Engineer Company: Visteon Corporation Job Category: Machine Learning Roles & Responsibilities: Overview of Role: The AI Forward Deployed Engineer partners directly with business stakeholders to identify high\-value opportunities and convert them into secure, scalable and measurable AI solutions. The role combines strong software engineering, generative AI and machine learning capability with stakeholder\-facing delivery. It owns the journey from discovery and rapid prototyping through enterprise integration, production deployment, adoption and continuous improvement. KPIs: Key Year One Deliverables: * Opportunity and Current\-State Assessment: Assess priority business opportunities, existing AI capabilities, data sources, platforms, integrations and delivery constraints. * Stakeholder Needs Analysis: Define business objectives, user needs, success measures, adoption barriers and technical requirements with key stakeholders. * Prioritized Delivery Roadmap: Create and maintain a roadmap based on business value, feasibility, data readiness, risk and resource needs. * Prototypes and Proofs of Concept: Develop rapid prototypes to validate technical feasibility, user value and solution direction. * Production AI Solutions: Build, deploy and operationalize selected AI solutions with appropriate security, scalability, reliability and support readiness. * Enterprise Integration: Integrate AI capabilities with enterprise applications, identity systems, APIs, data sources and operational workflows. * Evaluation and Observability: Establish evaluation, error\-analysis, logging, metrics, tracing, usage and cost\-monitoring mechanisms. * Documentation and Enablement: Deliver architecture documentation, runbooks, reusable patterns, training and knowledge transfer for users and support teams. * Software Engineering: Strong Python programming; backend and API development using FastAPI, Flask REST or equivalent; REST APIs, SQL, relational databases, Git, testing, debugging and code review. * Generative AI and Context Engineering: Large language models, prompt and context engineering, RAG and Graph RAG, embeddings, semantic search, vector databases, tool/function calling, memory, agent orchestration and evaluation. * Machine Learning: ML and deep\-learning fundamentals, natural language processing, model evaluation, data preprocessing and experience with PyTorch and/or TensorFlow. * Production Engineering: Containerization, CI/CD, cloud or on\-premises deployment, observability, performance tuning, scalability, resilience and production troubleshooting. Familiarity with multi\-model architectures, AI gateways, model routing, token and cost management, and model selection is preferred. * Data and Integration: Data pipelines, enterprise application integration, identity and access management, secure configuration and interoperability across systems. * AI Quality and Governance: Evaluation frameworks, error analysis, guardrails, responsible AI, data privacy, security, access control, lifecycle management and usage/cost monitoring. * Forward Deployed Delivery: Stakeholder discovery, problem decomposition, rapid prototyping, ambiguity management, end\-to\-end ownership, documentation and knowledge transfer. * Good to Have: Microsoft Azure and Azure OpenAI; LangGraph, LangChain or comparable frameworks; Qdrant, Milvus, pgvector or FAISS; Docker, Kubernetes, PostgreSQL and Redis; OpenTelemetry, Prometheus and Grafana; LiteLLM or comparable AI gateways. * Customer and Business Focus: Builds trusted stakeholder relationships and keeps solutions anchored to measurable user and business outcomes. * Ownership: Takes responsibility from discovery through production stabilization, adoption and continuous improvement. * Problem Solving: Uses structured analysis and hands\-on troubleshooting across application, model, data, integration and infrastructure layers. * Agility and Adaptability: Works effectively with ambiguity, changing priorities and evolving AI technologies. * Collaboration and Communication: Explains complex topics clearly and works effectively across Product, Engineering, Architecture, Infrastructure, Security and business teams. * Continuous Learning: Actively evaluates new approaches while applying sound engineering judgment, governance and evidence\-based decision\-making. * Attention to Detail: Maintains high standards for quality, security, documentation and operational readiness. This role reports to the Innovation department lead. If you thrive in a fast\-paced organizational culture that requires agility, adaptability and a growth mindset to stay ahead of the curve, Visteon is the place. We value high performance and a drive for results. Innovation, risk\-taking and continuous learning help us keep pace with the ever\-changing landscape of our industry and remain market leaders. At Visteon, you can be more. Work Location: Coimbatore South, Tamil Nadu, India Availability: Full time

Qualifications: * Bachelor's degree in Computer Science , Engineering, Data Science or a related technical discipline, or equivalent practical experience. * Hands\-on experience developing software, machine learning or generative AI solutions through internships, academic projects or professional environments; production deployment experience is preferred. * Demonstrated ability to translate business requirements into practical technical solutions and communicate effectively with technical and non\-technical stakeholders. Experience and Skills: Key Skills: Machine Learning, Python, TensorFlow, PyTorch Salary: Not disclosed