Job highlights

- Experience: 12+ years in AI / ML leadership,10+ years overall in tech Money Forward India is looking for a visionary and hands-on *AI Engineerto drive innovation,productivity,and strategic alignment across global engineering,product,and stakeholder teams .
- 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 explicit 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 ForwardIn-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

📌 AI Engineer Innovation (Chennai)
🏢 Money Forward
📍 Chennai