AI Engineer - Innovation & Productivity

Money Forward; Chennai District; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

Money Forward India is seeking a visionary AI Engineer to drive AI initiatives across global engineering, product, and stakeholder teams. You will own the AI SDLC, design context strategies, and build scalable prompts and agents for production-ready features.

You will lead cross-functional efforts to integrate AI into HR SaaS journeys, define success metrics, and guide colleagues in productionizing AI behavior with robust eval tooling and guardrails.

Qualifications

• Experience in ML/DL, RL, and AI systems with production readiness.
• Strong understanding of prompt engineering and context engineering.
• Knowledge of end-to-end AI/ML pipelines and governance.
• Hands-on with major ML frameworks and vector databases.
• Experience leading AI initiatives in SaaS or enterprise platforms.

Responsibilities

• Own the AI SDLC for Product Features.
• Design use cases, success metrics, and release gates.
• Architect prompts, tools usage, boundaries, and fallbacks.
• Create evaluation plans and automated tests.
• Guide engineers to productionize AI behavior and platform improvements.
• Build and extend AI features across HR SaaS products.
• Prototype independently and deliver production-ready specs.

Skills

Machine Learning

Deep Learning

Reinforcement Learning

Agentic AI

Prompt Engineering

Context Engineering

RAG

AI SDLC

Product mindset

Mentoring engineers

Tools

TensorFlow

PyTorch

Keras

Scikit-learn

Hugging Face

LangChain

Pinecone

Weaviate

Neo4j

PostgreSQL

OpenAI API

Job description

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

• 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.

Requirements

Qualifications

• AI / ML Domains
  • 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
  • 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 to production scale