AI Engineer - Innovation & Productivity

Money Forward; Pune District, Chennai District; Hybrid; INR 1,800,000 - 2,200,000; Full time

Job summary

Money Forward India invites an AI Engineer to lead the AI SDLC across product features and HR SaaS offerings. You will define use cases, design context strategies, and build robust prompt/agent patterns for scalable adoption.

You will mentor engineers, optimize model behavior, and drive guardrails, evaluation tooling, and governance to deliver production-ready AI capabilities.

Qualifications

• Experience in AI/ML domains and deploying ML systems.
• Ability to shape AI feature development across products.
• Proven track record of productionizing AI models.

Responsibilities

• Own the AI SDLC for Product Features with defined use cases and metrics.
• Build AI features across HR SaaS products and ensure scalable patterns.
• Prototype and deliver production-ready specs; document interfaces and constraints.
• Guide engineers to productionize AI behavior and monitor tool failures.

Skills

Machine Learning

Deep Learning

Reinforcement Learning

Prompt Engineering

Context Engineering

RAG

LLMs

Product Mindset

Team Leadership

Tools

TensorFlow

PyTorch

Keras

Scikit-Learn

OpenCV

Pandas

NumPy

Hugging Face Transformers

LangChain

LangGraph

LlamaIndex

Pinecone

FAISS

Weaviate

Milvus

PostgreSQL

MySQL

Neo4j

GPT/BERT/T5/LLaMA

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

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 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.

Requirements

Qualifications
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
• 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