Overview

• TensorFlow, PyTorch, Keras, Scikit-learn

Responsibilities

• 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 Forward’s 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.

Qualifications

• 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.
• 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
• TensorFlow, PyTorch, Keras, Scikit-learn
• OpenCV
• Pandas, NumPy
• Hugging Face Transformers
• GPT, BERT, T5, LLaMA
• YOLO, GANs
• LangChain, LangGraph
• CrewAI
• LlamaIndex
• DeepEval, LangSmith
• Vector Databases: FAISS, Pinecone, Weaviate, Milvus
• Relational Databases: PostgreSQL, MySQL
• Graph Databases: Neo4j
• NLP fundamentals, Word2Vec, embeddings
• NLTK, SpaCy
• Text classification, summarization, NER, semantic search
• Watson Discovery
• Watsonx.ai
• Watsonx.orchestrate
• Watsonx.data
• Watsonx.gov
• Model selection, fine-tuning, and prompt iteration
• CI/CD for AI systems
• Model and prompt versioning

Benefits

• Startup-like Work Environment & Culture
• Flexible Work Hours & Hybrid Work Policy (WFH 2 days a week)
• Leaves: Casual, Earned, and Sick leaves
• Up to 6 months of Maternity Leave
• Casual Dress Code (Shorts, Slippers, Sandals: All OK!)
• Corporate Health Insurance (Covering spouse, kids, and parents)
• Performance Review Twice a Year (Salary can be increased twice)
• Performance-Based Bonus
• Global Work Environment
• MacBook for All Employees
• Chance to Visit Japan & Vietnam Offices on Business Trip