AI Foundry BU Deployed Engineer

Overview

The AI Foundry Forward Deployed SBU Engineer bridges AI platform capabilities and real-world financial workflows, designing, prototyping, deploying and scaling AI-native solutions for specific business units and strategic client opportunities.

What you'll do

• Partner directly with Banking, Wealth, Buy-Side, and Research teams to identify high-value AI opportunities.
• Design AI-native workflows that solve real user problems rather than simply embedding AI into existing products.
• Deconstruct existing products and applications into key high-value primitives to fuel AI agents and workflows (APIs, MCPs / Tools, Agents, and more).
• Serve as the technical lead for strategic pilots, proofs of concept, and forward-deployed engagements.
• Develop reusable patterns that can transition from custom solutions into scalable platform capabilities.
• Build agentic workflows using Foundry platforms, APIs, MCPs, evaluation frameworks, and agent infrastructure.
• Design prompt, tool, memory, and workflow architectures.
• Create deterministic, auditable workflows suitable for institutional financial clients.
• Leverage evaluation frameworks to improve quality, repeatability, and trustworthiness.
• Prototype and validate new solutions rapidly with end users.

What you'll need

• 5+ years experience building applications in Finance.
• Strong understanding of at least one of: Wealth Management, Investment Banking, Asset Management, Buy-Side Research, Institutional Portfolio Management.
• 1+ years experience building AI-based applications.
• Required: Web Development or Python.
• Required: APIs, MCP Implementations, and Tool Calling Architectures.
• Required: LLMs, prompt engineering, agent architectures, vector search / RAG, evaluation frameworks, workflow orchestration, cloud-native development.
• 10+ years experience building software applications.
• Required: Solution architecture, security and governance, software development lifecycle, production deployment experience, CI/CD, containerization.
• Enjoys creating ambiguous zero-to-one solutions.

Nice to have

• Preferred understanding of securities, equities, fixed income, ETFs, mutual funds, and alternative investments.
• Preferred understanding of portfolio analytics, risk analytics, benchmarks, and performance attribution.
• Preferred: Multi-agent systems, knowledge graphs, retrieval systems, fine-tuning concepts, agent memory systems, AI governance frameworks.

Details

• Location: Hyderabad, India.