AI Engineer Lead

Capital Group•Irvine, CA

1d•$179,273 - $286,837•Hybrid

About The Position

As an 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.

Requirements

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 understanding of system design, APIs, distributed-systems concepts, and cloud-native development, with a track record of owning production systems on solid architectural foundations.
A disciplined approach to evaluation and testing for non-deterministic systems — you build evals and guardrails as a first-class part of the work, not an afterthought.
Strong communication skills: you can lead technical discovery, write clearly, and convey technical concepts to mixed audiences while keeping a low ego and a collaborative approach.
High agency and comfort navigating the ambiguity of a large, regulated organization, with the judgment to make trade-offs between scope, speed, and quality.
Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Experience implementing security, privacy, and compliance controls in production systems — for example IAM, encryption, audit logging, and data-governance practices, ideally in a regulated environment.

Responsibilities

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 FinOps 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 InfoSec 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.