Principal AI Engineer

Our client is a PE-backed holding company that acquires mid-market software businesses and rebuilds them around AI: embedding the technology into core operations to expand margin and into the product itself to restart growth. The firm has built a central technology capability to support that work across its portfolio, giving its engineering teams a common foundation for deploying increasingly sophisticated AI systems into very different businesses.

This is a senior hands-on individual contributor role spanning that central technology layer and the transformation work happening inside individual companies. You will build the infrastructure and tooling behind the firm's AI systems, then work directly with engineering teams across the portfolio when a problem requires deeper technical involvement.

The work is broad by design. One week you may be improving the architecture behind how agents execute complex tasks, the next you may be inside an unfamiliar software business figuring out where AI can materially change how its engineering organization operates. This is applied AI in a commercial setting, with a strong bias toward production systems, engineering quality and measurable business impact rather than research for its own sake.

Job responsibilities

• Build the core infrastructure behind production AI agents, including how they execute tasks, retain and use context, interact with external systems and coordinate work

• Develop systems that allow AI to take on increasingly complex software engineering work while maintaining appropriate controls around execution, access and human oversight

• Build internal tooling that helps engineering teams understand unfamiliar software estates, identify technical constraints and determine where AI can create the most leverage

• Put the controls around autonomous AI systems needed to run them safely in real production environments, including isolation, permissions, spend management and escalation paths

• Build abstraction layers that allow the underlying models and supporting infrastructure to evolve without forcing downstream applications to be repeatedly rebuilt

• Develop the testing, measurement and observability needed to understand whether AI systems are performing reliably, where they fail and what they cost to operate

• Work directly with portfolio engineering teams on difficult transformation problems, from understanding the existing technical environment through to designing and shipping the initial solution

• Improve the economics and performance of deployed AI systems by making informed choices across models, infrastructure and techniques for adapting models to particular workloads

Candidate requirements

• 7+ years of software engineering experience, including meaningful platform, infrastructure or other complex production systems work

• Deep Python expertise and strong experience with cloud infrastructure, containers and distributed systems

• Hands-on experience building production AI or agentic systems beyond straightforward LLM API integration

• Strong systems design judgment, particularly around reliability, observability and failure modes in non-deterministic systems

• Comfortable operating in ambiguous, fast-moving environments and taking problems from architecture through implementation

• High standards around code quality, testing and operational ownership

• Clear technical views on agent architecture, evaluation, model selection, cost and build-versus-buy decisions

• Able to get up to speed quickly on unfamiliar software environments and work credibly with senior engineering leaders

• Experience with developer tooling, legacy software or technology transformation would be useful

• Candidates must be based in the Los Angeles area; this is a hybrid role

Compensation

$150,000 to $350,000 base, plus equity