AI Engineer

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 strong 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