AI Engineer Lead

AI Engineer Lead

You are more than a job title. We want you to feel comfortable doing great work and bringing your best, authentic self to everything you do. We value your talents, traditions, and uniqueness—and we're committed to fostering a strong sense of belonging in a respectful workplace.

We intentionally seek diverse perspectives, experiences, and backgrounds, investing in a culture designed to celebrate differences. We believe that belonging leads to better outcomes and a stronger community of associates united by our mission. At Capital, we live our core values every day:
Integrity, Client Focus, Diverse Perspectives, Long-Term Thinking, and Community.

As a AI Engineer Lead, you'll partner with investment professionals and business partners to turn ambiguous problems into working AI solutions that improve our investment process and business outcomes. You are the build-and-deploy bridge between the people who own the problem and the AI platform that powers the answer: you lead discovery, design the approach, write the code, and own it in production.

This is a hands-on engineering role for someone who is as comfortable in a working session with a business team as they are building a retrieval pipeline or hardening an agent. You will help set the standard for how generative AI gets built and operated responsibly at scale across the firm.

You will

• Partner directly with business partners to understand their workflows, scope the highest-value opportunities, and translate ambiguous needs into clear technical specifications.
• Design, build, and operate production generative AI applications — copilots, assistants, knowledge-search experiences, and agentic workflows — as reliable, production-grade systems rather than demos.
• Architect and implement end-to-end retrieval-augmented generation pipelines, including parsing, ingestion, chunking strategy, embeddings, vector storage, retrieval, and prompt management.
• Build agents and agentic workflows that plan and execute multi-step tasks within explicit, auditable boundaries, with guardrails that keep behavior safe and predictable.
• Practice eval-driven development: define acceptance criteria up front, build evaluation harnesses, and measure correctness, latency, and hallucination so quality is verifiable and regressions are caught before production.
• Take end-to-end ownership from discovery and design through build, rollout, and operational excellence — instrumenting systems with the observability, cost tracking, and audit trails needed to know when they degrade.
• Apply Fin Ops and cost-optimization practices to AI workloads, tracking and managing token, inference, and infrastructure spend so solutions stay cost-effective as they scale.
• Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment.
• Apply responsible-AI judgment proportionate to the risk of each use case, working with risk and compliance partners to build the controls, human-oversight patterns, and audit trails that let the firm move quickly and safely.
• Embed security, privacy, and compliance controls into the systems you build — including identity and access management (IAM), encryption, and audit logging — partnering with Info Sec and data-governance teams to meet regulatory and internal-policy requirements such as SOC 2 and applicable data-privacy regulations.
• Codify what works into reusable tools, patterns, and playbooks, and feed insights back to platform, product, and engineering partners so the whole organization gets faster.
• Produce clear documentation, runbooks, and architectural diagrams so the systems you build can be understood, operated, and extended by others.

Required qualifications

• Minimum 10+ years of professional software engineering experience, with strong proficiency in Python (or a comparable modern language).
• Hands-on production experience building and shipping LLM-powered applications, including advanced prompt engineering, retrieval, agent development, and evaluation.
• Demonstrated experience designing and building end-to-end RAG pipelines and integrating LLM solutions with real systems.
• Strong…