AI Engineer

Experience: 3.00 + years

Salary: Confidential (based on experience)

Expected Notice Period: 15 Days

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Office ()

Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: An investment management / asset management firm)

(*Note: This is a requirement for one of Uplers' client - An investment management / asset management firm)

What do you need for this opportunity?

Must have skills required:

Evaluation infrastructure for LLM outputs, MCP (Model Context Protocol) tools, LangGraph, Memory Management, Python, Rag model, Agentic AI, Financial Data, SQL/No-SQL Databases

An investment management / asset management firm is Looking for:

AI/Data Engineer (Investment Research)

About AR

Founded in 2006, we are an asset management company managing funds for global quality institutions. We have offices in Singapore and India. We are now building an AI engine to augment and scale our fundamental equity research capabilities, and this is one of the first hires on that team.

About The Role

You will be joining a brand new AI team at AR, working directly with Yasin Rosowsky (Head of AI, PhD in AI from UCL) to build the data and agent infrastructure that powers our equity research platform. This is a hands-on, high-ownership role — you will be designing and building systems from scratch, working closely with investment analysts, and iterating fast.

If you''ve worked at companies like AlphaSense, or have built AI systems on top of financial data inside asset managers, hedge funds, or quant firms — this role was written for you.

Role Overview

Build and maintain the data infrastructure and agent systems that underpin fundamental equity investment research — supporting the investment process from initial idea generation through deep-dive research to portfolio management. The role is hands-on across pipelines, agent workflows, data systems, MCP tools, and evaluation infrastructure.

Key Responsibilities

Data Pipeline Development Build and maintain pipelines that ingest, clean, and structure financial data from multiple sources — market data, filings, earnings transcripts, macro feeds — handling the inconsistencies, latency differences, and structural complexity that come with real-world financial data at scale.

RAG & Knowledge Graph Development Build and maintain retrieval systems and knowledge graphs that give agents accurate, structured access to financial data. Continuously iterate on retrieval quality — improving how data is chunked, indexed, and surfaced — based on agent performance and input from investment analysts.

Agent System Development Build and implement multi-agent systems capable of carrying out complex, multi-step research workflows — coordinating specialist agents across retrieval, reasoning, and synthesis, and managing state and context across long-running runs. Work within the broader system architecture to ensure agents access tools and data correctly, and that outputs are structured for downstream use.

Evaluation & Iteration Instrument agent runs to capture structured output that supports systematic review and failure analysis. Identify failure patterns — retrieval errors, reasoning gaps, data quality issues — and iterate continuously. Work closely with investment analysts to understand where outputs are falling short, and incorporate their feedback into retrieval systems, tooling, and agent behaviour on an ongoing basis.

Frontend & API Integration Connect agent systems and data infrastructure to front-end applications via clean, reliable APIs. Wrap data sources, financial models, and external services into versioned tools that agents and downstream systems can depend on.

What We''re Looking For

Someone with a genuine curiosity about how businesses work and how capital is allocated — and the drive to build systems that fundamentally change the depth and scale at which investment research can be done. We value candidates who have built LLM-based solutions as a core part of their practice and who are excited by the hard problem of making rigorous, large-scale investment research possible through AI.

Requirements

Experience 3–6 years in data engineering, ML infrastructure, or backend systems, with recent hands-on experience building LLM agent systems in the context of financial or deep research applications. Experience working with equity research data — filings, earnings transcripts, pricing feeds, financial statements, and consensus estimates — is a strong plus.

Education Master''s or PhD in Computer Science, Engineering, or a related quantitative field preferred. Strong portfolios of shipped projects or open-source contributions are equally welcome.

Agent Systems Hands-on experience with agentic frameworks (LangGraph, Google ADK, or equivalent). Practical understanding of prompt chaining, tool use, memory, and multi-agent orchestration.

RAG & Knowledge Graphs Experience building and iterating on retrieval-augmented generation systems and knowledge graphs. Able to diagnose how retrieval quality affects agent output.

Evaluation Experience building evaluation infrastructure for LLM systems — structured failure analysis and tight iteration cycles with domain experts.

Engineering Strong in Python. Comfortable with SQL, ETL pipelines, and building APIs. Familiarity with AWS, Azure, or GCP is a plus.

Strong Plus

• Experience at companies like AlphaSense, Visible Alpha, FactSet, or similar fintech platforms built for fundamental research
• Background in asset management, hedge funds, or quantitative research divisions of investment banks
• Experience with MCP tools and integrations
  
  
  

What You''ll Get

• Work directly with a seasoned AI leader (PhD, UCL) with 15 years in AI and investment management
• Be part of a small, high-calibre founding team at a prestigious asset management firm
• Real ownership from day one — this isn''t a support role
• Mumbai office, 5 days/week — Yasin visits once a month in person
  
  
  

How to apply for this opportunity?

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
  
  
  

About Uplers:

Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.

(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).

So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!