AI Engineer - Innovation & Productivity

As an AI Engineer at Money Forward India, you will play a crucial role in driving innovation, productivity, and strategic alignment across global engineering, product, and stakeholder teams. Your responsibilities will include but not be limited to: - Own the AI Software Development Life Cycle (SDLC) for Product Features - Define use cases, scope, success metrics, and release gates - Design context strategy for structured inputs, retrieval, redaction - Build prompt and agent designs with clear tool usage, boundaries, and fallbacks - Create evaluation plans including golden sets, rubrics, and automated regression tests - Support rollout, monitoring, and continuous improvement of AI features across multiple HR SaaS products - Incorporate AI into core user journeys such as onboarding, employee communications, policy Q&A, document generation, and support workflows - Maintain reusable prompt/agent patterns across teams and products for scalability - Prototype independently and deliver production-ready specifications - Build proof of concepts (POCs) to prove feasibility and user value - Convert POCs into production specs including interfaces, constraints, failure modes, and acceptance criteria - Guide engineers in productionizing AI behavior by translating product intent into implementable AI specs and reviewing implementations - Help debug model behavior, tool failures, and edge cases for optimization - Drive adoption of prompt versioning, evaluation harnesses, and monitoring best practices - Utilize and extend Money Forwards in-house AI platform by building with internal models, orchestration frameworks, and shared components - Propose improvements for prompt management, evaluation tooling, logging, and guardrails for platform growth Qualifications Required: - Machine Learning, Deep Learning, Reinforcement Learning - Agentic AI systems and multi-agent orchestration - 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, token management, governance, security, compliance Preferred Experience: - Experience leading AI initiatives in SaaS or enterprise platforms - Strong product mindset with the ability to balance UX, reliability, and cost - Experience mentoring senior engineers and AI practitioners - Proven track record of taking AI systems from idea to production to scale At Money Forward India, you will enjoy a startup-like work environment with a flexible work policy, casual dress code, and numerous benefits including health insurance, maternity leave, performance-based bonuses, and a global work environment. Join us to be part of a dynamic, growth-oriented environment where innovation thrives and professional development is encouraged. As an AI Engineer at Money Forward India, you will play a crucial role in driving innovation, productivity, and strategic alignment across global engineering, product, and stakeholder teams. Your responsibilities will include but not be limited to: - Own the AI Software Development Life Cycle (SDLC) for Product Features - Define use cases, scope, success metrics, and release gates - Design context strategy for structured inputs, retrieval, redaction - Build prompt and agent designs with clear tool usage, boundaries, and fallbacks - Create evaluation plans including golden sets, rubrics, and automated regression tests - Support rollout, monitoring, and continuous improvement of AI features across multiple HR SaaS products - Incorporate AI into core user journeys such as onboarding, employee communications, policy Q&A, document generation, and support workflows - Maintain reusable prompt/agent patterns across teams and products for scalability - Prototype independently and deliver production-ready specifications - Build proof of concepts (POCs) to prove feasibility and user value - Convert POCs into production specs including interfaces, constraints, failure modes, and acceptance criteria - Guide engineers in productionizing AI behavior by translating product