AI Engineer Innovation & Productivity (Chennai)

Experience: 12+ years in AI/ML leadership, 10+ years overall in tech Money Forward India is looking for a visionary and hands-on AI Engineer to drive innovation, productivity, and strategic alignment across global engineering, product, and stakeholder teams. This role blends deep technical expertise with cross-functional leadership to accelerate feature development, promote AI adoption, and shape the future of intelligent systems within our organization.Responsibilities The successful candidate will be responsible for, but not limited to, the following key tasks: Own the AI SDLC for Product Features Strategic Definition: Define use cases, scope, success metrics, and release gates. Context Engineering: Design context strategy (structured inputs, retrieval/RAG, redaction). Architecture & Logic: Build prompt and agent designs with clear tool usage, boundaries, and fallbacks. Quality Assurance: Create evaluation plans (golden sets, rubrics, automated regression tests). Lifecycle Management: Support rollout, monitoring, and continuous improvement. Build AI Features Across Multiple HR SaaS Products Integration: Incorporate AI into core user journeys (onboarding, employee comms, policy Q&A, document generation, support workflows). Scalability: Maintain reusable prompt/agent patterns across teams and products. Prototype Independently and Deliver Production-Ready Specs Feasibility: Build POCs and thin vertical slices to prove feasibility and user value Documentation: Convert POCs into production specs: interfaces, constraints, failure modes, and acceptance criteria. Guide Engineers to Productionize AI Behavior Implementation: Translate product intent into implementable AI specs and review implementations. Optimization: Help debug model behavior, tool failures, and edge cases. Best Practices: Drive adoption of prompt versioning, eval harnesses, and monitoring. Use and Extend Money Forwards In-house AI Platform Development: Build with internal models, orchestration frameworks, and shared components. Platform Growth: Propose improvements for prompt management, eval tooling, logging, and guardrails. RequirementsQualifications Required Technical Expertise AI / ML Domains Machine Learning, Deep Learning, Reinforcement Learning Agentic AI systems and multi-agent orchestratio Anomaly Detection, Recommender Systems AI-assisted engineering (Claude Code, Cursor, Copilot-style workflows) Prompt Engineering, Context Engineering, RAG Frameworks & Libraries Core ML / CV / NLP TensorFlow, PyTorch, Keras, Scikit-learn OpenCV Pandas, NumPy LLMs & Transformers Hugging Face Transformers GPT, BERT, T5, LLaMA YOLO, GANs Agent & LLM Orchestration LangChain, LangGraph CrewAI LlamaIndex DeepEval, LangSmith Databases & Data Stores Vector Databases: FAISS, Pinecone, Weaviate, Milvus Relational Databases: PostgreSQL, MySQL Graph Databases: Neo4j NLP & Language Technologies NLP fundamentals, Word2Vec, embeddings NLTK, SpaCy Text classification, summarization, NER, semantic search IBM Watson Suite Watson Discovery Watsonx.ai Watsonx.orchestrate Watsonx.data Watsonx.gov AI/LLM SDLC, Deployment & Operations Model selection, fine-tuning, and prompt iteration CI/CD for AI systems Model and prompt versioning Online and offline evaluation pipelines Observability (logs, traces, metrics) Cost optimization and token management Governance, security, and compliance Preferred Experience Experience leading AI initiatives in SaaS or enterprise platforms Strong product mindset with ability to balance UX, reliability, and cost Experience mentoring senior engineers and AI practitioners Proven track record of taking AI systems from idea production scale BenefitsWhy Jo .