Staff AI Engineer

Staff Forward Deployed AI Engineer 

AI strategy and integration firm that builds production AI systems for B2B companies in private equity, wholesale lending, real estate, and SaaS. We do not build decks about what AI could do. We ship systems that move revenue, compress costs, and automate the work that used to take entire teams. 

The Role  

As Staff AI Engineer, you are the technical leader and backbone of our San Diego engineering team. At this level, your job is no longer just what you build; it is making sure every engineer around you builds better. You mentor and manage engineers, set the standard for how the team designs, reviews, and ships AI systems, and own their growth as deliberately as you own your own code. 

You pair that leadership with serious technical depth. You stay hands-on with the hardest builds in our client portfolio: multi-step agent systems, complex document processing pipelines, and automations that touch core revenue operations. When a problem has ambiguous requirements or no obvious solution, the team looks to you, and clients measure the result in EBITDA impact, not demos. 

You report to the Head of Client Engineering, and you will occasionally join client calls to walk stakeholders through what we are building and why. You thrive here if leveling up the people around you energizes you as much as shipping the system yourself. 

What You'll Do  

You are on the forefront of the technology, reimagining how we do AI and delivering the high-value solutions that set us apart with clients. 

Tea m Leadership and Mentorship 

• Manage and mentor two engineers on the San Diego team, owning their technical growth, code quality, and delivery. 

• Set the engineering bar through rigorous code review, pairing, and reference implementations the rest of the team builds on. 

• Serve as the first escalation point when engineers hit problems they cannot solve alone, teaching as you unblock. 

• Build the habits, patterns, and practices that make every engineer on the team measurably better quarter over quarter. 

Technical Direction  

• Drive architecture decisions across client builds: agentic workflows, RAG pipelines, document processing, and automation running in production. 

• Evaluate new models, frameworks, and techniques as they emerge, and decide what enters our production toolkit. 

• Turn hard-won lessons from client builds into reusable internal tooling, templates, and documentation that compound across engagements. 

Build and Ship Production AI Systems  

• Stay hands-on with our most complex client systems, owning delivery end to end from LLM orchestration and evaluation to data pipelines, APIs, and cloud infrastructure. 

• Make pragmatic build decisions under real constraints: client timelines, legacy systems, messy data, and measurable business outcomes on the line. 

• Ship fast without sacrificing reliability; design systems that keep working long after the engagement ends. 

Client Collaboration  

• Join client calls when the conversation needs technical depth, walking stakeholders through system designs, trade-offs, and progress. 

• Translate ambiguous business problems into concrete system designs and delivery plans clients can commit to. 

• Flag risks, trade-offs, and scope realities directly and early, protecting both the client outcome and the team's delivery. 

What You Need to Succeed  

• 8+ years of software engineering experience, including 2+ years building LLM-powered systems (RAG, agents, structured extraction) that run in production, not just prototypes or internal experiments. 

• Demonstrated technical leadership: engineers you have mentored or managed got measurably better because of your code review, pairing, and guidance, and you want direct reports as part of your role. 

• Expert-level Python and strong full-stack fundamentals, with the judgment to choose boring, reliable technology when the problem calls for it. 

• Production cloud experience (AWS, GCP, or Azure), including deploying, monitoring, and maintaining systems that clients depend on daily. 

• Deep working knowledge of the modern AI stack: prompt and context engineering, evaluation, embeddings, vector search, and agent orchestration frameworks. 

• A track record of owning ambiguous, high-stakes projects end to end, from problem framing through architecture, build, and post-launch iteration. 

• Fluent English and clear communication on client calls; you can explain an architecture decision to a non-technical executive without losing them. 

• Consulting-environment adaptability: comfort switching between client contexts, industries, and codebases without losing momentum. 

• Based in San Diego, CA with reliable overlap with Colombia business hours for client and team collaboration. 

• An AI-first working style: you use these tools daily to multiply your own output and teach the team to do the same.