- 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
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
- 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 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
- Online and offline evaluation pipelines
- Observability (logs, traces, metrics)
- Cost optimization and token management
- Governance, security, and compliance
- 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
Benefits
Why Join Us?
At Money Forward India, you'll be part of a dynamic, growth-oriented environment where innovation thrives. Our culture fosters collaboration, creativity, and professional development. Join us to be at the forefront of SaaS technology, with the security of an established corporate and the agility of a startup.
- 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
Skills Required
- 12+ years of experience in AI/ML leadership
- 10+ years of overall technology experience
- Experience with machine learning, deep learning, and reinforcement learning
- Experience with agentic AI systems and multi-agent orchestration
- Experience with anomaly detection and recommender systems
- Experience with AI-assisted engineering workflows using Claude Code, Cursor, or Copilot-style tools
- Experience with prompt engineering, context engineering, and RAG
- Experience with TensorFlow, PyTorch, Keras, Scikit-learn, OpenCV, Pandas, and NumPy
- Experience with Hugging Face Transformers, GPT, BERT, T5, LLaMA, YOLO, and GANs
- Experience with LangChain, LangGraph, CrewAI, LlamaIndex, DeepEval, and LangSmith
- Experience with FAISS, Pinecone, Weaviate, Milvus, PostgreSQL, MySQL, and Neo4j
- Knowledge of NLP fundamentals, Word2Vec, embeddings, NLTK, spaCy, text classification, summarization, NER, and semantic search
- Experience with Watson Discovery, Watsonx.ai, Watsonx Orchestrate, Watsonx.data, and Watsonx.gov
- Experience with AI system deployment, CI/CD, model and prompt versioning, evaluation pipelines, observability, cost optimization, governance, security, and compliance
- Experience leading AI initiatives in SaaS or enterprise platforms
- Strong product mindset balancing user experience, reliability, and cost
- Experience mentoring senior engineers and AI practitioners
- Track record of taking AI systems from idea through production and scale
What We Do
Money Forward, Inc. is a Japanese SaaS financial technology company that develops personal finance management tools and cloud-based services for individuals, businesses, accounting professionals, and financial institutions. Its platform supports household finance, accounting, tax, expense management, payments, and other back-office operations. The company serves consumer and corporate markets through integrated digital products and aims to operate as a comprehensive technology-driven financial platform.

.png)






