Forward Deployed AI Engineer - Lead

Forward Deployed AI Engineer – Lead

Role Overview

As a Forward Deployed AI Engineer (FDE) for our Chicago hub, you will operate at the front lines of the AI revolution, and act as an elite technical bridge between our core research breakthroughs and our most strategic customers. Unlike traditional software engineers, you are embedded directly within client environments to solve their hardest, highest-stakes problems by building and shipping production-grade Agentic AI systems. You operate with the autonomy of a founder, and the technical rigor of a staff engineer to turn messy, real-world data into measurable business value.

This is a Senior IC role with leadership scope. You will own the entire lifecycle of AI deployment—from initial discovery and scoping to the development of full-stack systems and guiding the path to client integration. Your success is measured not just by the code you write, but by the client adoption and the workflow impact realized by the client. A pattern you build for one customer becomes an abstraction for many more. Note that this role requires you to have a BFSI industry background to be effective from day one.

Key Responsibilities

AI Deployment & Production Engineering

• Lead the client technical engagement and the POD delivering it: Executive communication, reference architectures, alongside building POCs with teammates.
• Drive prototype to production system buildout through tight iteration and feedback cycles, ensure constant learning from evals and demos.
• Collaborate with the larger engineering team to build AI capabilities in the enterprise context.
• Optimize AI workflows for performance, low latency, and reliability under real-client constraints.
• Harden systems for security and compliance, ensuring models operate safely within regulated environments.
• Utilize advanced AI coding assistants or agentic development tools such as Claude Code and OpenAI Codex, regularly to meet client needs.

Embedded Problem-Solving & Feedback

• Embed deeply with customer engineering teams to translate open-ended business challenges (e.g., "reduce fraud") into concrete technical solutions.
• Act as the primary technical owner, troubleshooting complex production issues and serving as the last line of defense in the field.
• Use feedback from the field: failure modes, eval results, latency profiles, and adoption patterns to drive roadmap inputs for the R&D teams.
• Codify successful field patterns into reusable tools and abstractions; actively contribute to the AI foundational platform within Altimetrik.

Relevant Industry Experience

• P&C (Property and Casualty) Insurance Expertise (Underwriting, Operations, Call Center, Claims)
• Understanding of business (BFSI) that require intensive governance, compliance and security when it comes to AI execution and implementation

Who You Are

• A hands-on builder with a "radical ownership" mindset—you don't file tickets; you fix problems across the full stack.
• Comfortable with C-level dialogue and capable of translating complex AI systems into business language for non-technical executives.
• Industry veteran: 15 years of hands-on AI / Data / Software engineering experience
• Highly proficient in Python, SQL, and modern orchestration frameworks
• Fluent in production work across at least one of the major LLM platforms: Anthropic, OpenAI, Google Gemini or AWS Bedrock
• You’ve built agentic workflows, RAG systems, and custom data pipelines within an enterprise environment.
• Have shipped production systems on at least public cloud (AWS, Azure or GCP), navigated IAM, Data Services, Security and Network controls.
• Able to work at the local client location and travel up to 30% of your time. Sustained on-site interaction is a key differentiator for the FDE role.

Preferred / Nice to have

• Hands-on experience building applications within a modern data platform: e.g. Snowflake, Databricks, Google BigQuery.
• Prior experience building production AI or data systems in a regulated vertical: BFSI, Payments, FinTech, or Life Sciences. Hands-on familiarity with relevant risk, compliance, and audit frameworks.
• Published writing or open-source contributions in the agentic AI or data engineering space.