We’re at an inflection point in AI. Codegen is accelerating development, foundation models are stronger and more accessible, and the barriers to building have never been lower.
But as building becomes easier, so too does building the wrong thing. For companies diving into AI, the real constraint isn’t technology—it’s clarity. The challenge has shifted from “Can we do this?” to “What’s worth building, and how do we make it work inside the messy realities of enterprise?”.
After years of hands-on building, it became clear to me that the most exciting challenges weren’t in model performance, but rather in the translation layer: integrating AI into the business so it actually drives change.
That’s why I shifted out of daily production code and into the role I’m in today: an AI Solutions Architect at Tribe. I help enterprises figure out what to build, how to build it, and how to embed AI into their workflows so it delivers impact. It’s fast, technical, and high-leverage, and more engineers should know this path exists.
Today, Most AI Engineering Roles Are Siloed
If you’re an AI engineer, you probably spend most of your time tucked away inside a product org, focused on a narrow slice of a much larger system.
You go deep, but often without ever seeing the bigger picture. Someone else scopes the problem. Someone else frames the opportunity. You’re handed a ticket, you build what’s asked, and you ship what’s defined.
You rarely talk to customers. You almost never talk to executives. And you’re certainly not talking to the teams at the frontier labs.
That was me. I loved the technical challenge, but I wanted more. I wanted to work across disciplines, influence the high-leverage decisions, and shape what got built, without losing the technical depth that I cared about.
Why This Role Is Different
1. Bridging Vision & Execution
As an AI Solutions Architect at Tribe, I’m not buried in tickets or limited to a single component of the stack. Instead, I partner with CTOs and business leaders to explore what’s possible and how to make it a reality.
I help evaluate:
- What’s worth building vs. what should be bought
- Where AI can actually drive business value
- How to architect systems that are robust and enterprise-ready
Once we align, I move fast, building a UI mockup, an agentic workflow, or an end-to-end system with real data and APIs. Sometimes it’s a call with Anthropic or OpenAI that unlocks a constraint and changes the entire approach.
I build 2-3 prototypes a week: fast, focused, high-touch. No grooming meetings. Just design, feedback, execution.
The work is still deeply technical. Each week, I:
- Design demos that integrate models, APIs, and enterprise workflows
- Build rapid prototypes to make abstract ideas concrete
- Map full-stack architectures from data ingestion to UX
- Evaluate trade-offs between cost, latency, and reliability
- Test the latest model releases in production contexts
The difference is fluency. This role is about bridging worlds (engineers, executives, and customers alike) and showing how systems actually work in practice.
2. Tackling Mission-Critical Initiatives
What excites me most about this role is the caliber of problems I get to work on. These aren’t R&D experiments or “let’s see what happens” POCs.
These are the systems that will define companies’ next generation of products, operations, and customer experiences.
I’ve partnered with enterprises to build:
- AI copilots embedded in internal systems that accelerate decisions in fields like finance, healthcare, and industrial operations
- Retrieval-augmented knowledge platforms that surface proprietary institutional data while meeting strict compliance and security standards
- Workflow automation systems that cut across legacy infrastructure, where reliability, governance, and auditability are make-or-break
- Consumer-facing AI products and features, from personalized experiences that engage millions of end users to entirely new revenue-generating product lines
What do these projects have in common? They don’t just need to work. They need to work under real-world constraints: regulatory compliance, fragmented data, legacy systems, strict security policies, cost ceilings, and the politics of large organizations. Navigating that complexity is this role’s superpower.
3. Partnering at the Frontier
One of the most unique aspects of this role is the proximity to the teams actually shaping the future of AI.
I’ve had the chance to sit in the room at OpenAI and Anthropic, collaborating with their Applied AI teams, solutions engineers, and GTM leaders. In those sessions, we’re sharing insights from the field, surfacing enterprise requirements, and testing edge cases that influence how these tools evolve.
That access means:
- Roadmap visibility: we see what’s coming before it’s public.
- Early experimentation: we get to try new capabilities while they’re still being shaped.
- Trusted context: we bring back those insights to our customers, helping them plan around not only what works today, but what will be possible tomorrow.
If you want to stay close to the frontier (not just read about breakthroughs on social media or in press releases) this role puts you in the loop, with a seat at the table where the next generation of AI is being defined.
Who This Role Is For
In six months, I’ve seen more enterprise AI builds than I did in years in-house: 200+ customer meetings, 50+ prototypes, countless cycles of feedback, iteration, and integration.
That pace doesn’t just build speed. It builds pattern recognition. I now know where AI adoption tends to break down, which design decisions create downstream risk, and how to explain trade-offs in a way both engineers and executives can act on. That kind of fluency is hard to get anywhere else.
You might be a great fit for this role if:
- You’re excited by the pace and density of learning that I just described
- You still love building, but want more exposure to strategy and customers
- You want to influence what gets built as much as how it gets built
- You’re fluent enough in both technical and business worlds to operate across them (or you’ve ever felt like you’re too technical for strategy roles and too strategic for engineering roles)
- You care about seeing AI not just get built, but get used
I used to think stepping away from the keyboard would mean giving up influence. Turns out, it gave me more.
If you’re an AI builder who wants to be in the room where the most important decisions are made, with enterprises betting on their future and with the labs shaping the frontier, this is your next move.
