About the Role

We are building a Generative AI-driven financial intelligence platform designed to help users — from novice investors to experienced market participants — make smarter financial decisions.

This is not a surface-level LLM integration role. We are looking for a strong Machine Learning Engineer who can work across classical ML + LLM systems, contribute to a multi-agent architecture, and build production-grade AI systems where data correctness and reliability are critical.

If you are a core ML engineer who thinks in systems — not scripts — this role is for you.

Key Responsibilities

- Design, build, and deploy production-grade ML systems
- Work on multi-model / multi-agent AI architecture
- Integrate classical ML models with LLM-based components
- Build and evaluate recommendation systems & predictive models
- Ensure data integrity and sanity checks (critical in finance)
- Own full lifecycle: Research → Experimentation → Evaluation → Production
- Optimize models for scalability, reliability, and performance
- Collaborate closely with product and engineering teams

Required Skills & Experience

Core Machine Learning

- Strong foundation in supervised learning, recommendations, predictive modeling
- Solid understanding of statistics & mathematical fundamentals
- Experience designing ML systems beyond basic model training




- Strong evaluation methodology knowledge

LLM & AI Systems

- Experience working with LLM-based systems
- Exposure to multi-agent or multi-model systems preferred
- Understanding of system-level architecture involving LLMs
- Experience beyond basic RAG implementations

Note: Only prompt engineering or surface-level RAG experience will not be sufficient.

Programming & Engineering

- Strong Python skills
- Strong data structures & problem-solving ability
- Ability to write clean, production-quality code
- Understanding of system design and scalability principles

Ideal Candidate Profile

- 2–6 years of relevant experience in ML/AI
- Strong tenure stability (2.5–4 years preferred in roles)
- Product company experience preferred
- Fintech domain exposure is a robust advantage
- Experience building systems end-to-end (ownership mindset)
- Strong engineering fundamentals (non-negotiable)

Red Flags (What We Avoid)

- Only prompt engineering experience
- Only RAG-based implementation without ML depth
- Service-based background with limited ownership
- Frequent job switching (

- Opportunity to build AI systems from scratch in a high-impact domain

- Ownership-driven culture

- Exposure to cutting-edge Generative AI architecture

- Fast feedback and high learning environment

📌 AI Engineer (Bengaluru)
🏢 Recro
📍 Bengaluru