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 (

Weak coding fundamentals What We Offer 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