AI Automations Product Engineer

About this role

NineTwoThree AI Studio is a premier product design, engineering, and marketing firm specializing in custom AI, web, and mobile applications for established brands and funded startups. We are based in Massachusetts but with an American and European staff and a strong, collaborative remote culture. We’re a team that loves doing good work with great people. Our relatively small size keeps us fast and nimble. The wealth of knowledge, experience and talent paired with proven recipes and best practices allows us to find opportunities to help new products succeed. With a portfolio of over 150 launched products over 13 years , NineTwoThree has garnered recognition as a top AI agency in the U.S., earning accolades such as inclusion in the Inc. 5000 list for four consecutive years and being named among the top 50 AI firms alongside industry leaders like Microsoft, NVIDIA, and IBM. We’ve built AI and ML tech for big brands like Consumer Reports, FanDuel, and Nara, as well as startups in legal tech, logistics, education, and more. Job description Building, shipping, and supporting AI-powered tools and automations for 923 AI Automation clients. The role sits at the intersection of product thinking and AI engineering — closer to “product manager who builds confidently with modern AI development tools” than “AI research engineer.” You'll work with tools like Claude Code, the Anthropic API, and AI-augmented low-code platforms to deliver real systems to real clients, end-to-end. You'll probably enjoy this role if you: Like framing complex, ambiguous problems and working through them to clear solutions Enjoy ideating, building, and seeing things ship — then explaining clearly how and why they work Prefer direct interaction with a mentor over distant management Want autonomy and trust, not oversight for its own sake Want to understand why you're building something, not just what to build Are energised by seeing the whole picture — how a project connects to a client's business, a team's operations, and an emerging practice You'll probably find this role frustrating if you: Prefer to work deep in code with minimal client or stakeholder contact — the role sits in the middle of client delivery, not behind it Are looking for a role with highly structured processes, detailed specifications, and predictable work — this department is actively shaping its own way of working, and the person in this seat will be part of that shaping Requirements 1. Strategy & Growth Pattern Contribution — Surfacing new approaches, tools, and workflows discovered during client delivery, and contributing them to the department's knowledge base and operating models. Ideas are validated on real client work before they become standard. Continuous R&D — Spending dedicated, reported hours testing new AI tools, models, and frameworks relevant to active or upcoming client work. Findings are shared with the Practice Lead and the wider department. Practice Building — Helping shape the way the department works as it scales — contributing to project templates, runbooks, evaluation approaches, and reusable components seeded from real delivered work. 2. Solutions Engineering & Client Delivery This is the centre of gravity of the role. You'll be working alongside the Practice Lead and Account Manager on scoping, shaping, and solutioning new client engagements, then owning the delivery against those shaped briefs. Discovery & Shaping Support — Joining discovery calls and shaping sessions with the Practice Lead to translate vague business problems into viable AI-driven solutions. Initially, you'll join these calls to learn the pattern; over time, you'll take more of them directly as direction and confidence grow. Technical Feasibility — Evaluating client data, systems, and APIs to determine what's genuinely achievable within a proposed time budget. Flagging risks, rabbit holes, and integration blockers early, before commitments are made to the client. Demo Build-Out — Building working demos for prospective clients that demonstrate the proposed solution against their real problem, ready to be presented on proposal calls. Proposal & Scope Input — Contributing technical detail to proposals and statements of work — architecture approach, milestones, success criteria — which the Practice Lead translates into client-facing documents. Client Onboarding & Education — Helping client teams understand and confidently use the systems we deliver. This includes walkthroughs, training sessions where relevant, and clear handover documentation. 3. Technical Execution You'll own the technical delivery of client projects end-to-end, inside the department's established approach. Project Setup & Architecture — Standing up new client projects — repository, environments, tooling, third-party accounts — following the department's conventions. Designing the architecture of individual projects in line with 923's engineering and deployment standards. System & Process Design — Designing the end-to-end system: how data flows in, how AI components fit into the pipeline, where humans sit in the loop, what the output looks like, and how the user actually engages with it. Thinking in terms of the job the system does for the client, not just the components that make it up. Agentic Design & Trade-offs — Making considered decisions about when to use agentic approaches versus deterministic pipelines versus human-in-the-loop steps. Understanding the trade-offs and being able to explain them clearly to the Practice Lead and to clients. Hands-on Build — Writing the code that makes the system work, using AI development tools fluently. Python-first stack, typically lightweight web frameworks (FastAPI, Gradio) and serverless runtimes (AWS Lambda). AI-augmented practice is expected and welcomed - the role is defined by what ships, and real progress for our clients. Prompt Engineering & Model Selection — Designing and refining the prompts, system instructions, and model choices that drive AI beh