The posting
About The Role:
- Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside it.
- Real tasks, not scope. You are not fenced into a siloed deliverable. You go where the operating problem is.
- Autonomous. Embedded is not staff-augmented. You own the method; nobody hands you a ticket.
What You'll Bring:
- 8+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way
- You will take the operator’s seat. You are genuinely willing to spend weeks doing someone else’s job — claims processing, underwriting, revenue-cycle work — before you write a line of code. Engineers who need to stay in the IDE should not apply
- You learn domains fast. Demonstrated ability to become conversant in an unfamiliar business function quickly enough to argue with the people who do it for a living
- Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works
- You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why
- Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack
- Cloud-native delivery on AWS (GCP/Azure a plus): containers, Kubernetes/ECS, IaC, CI/CD, and the operational reality of a system someone else inherits
- Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room
- Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job
- Solid AI/ML foundations — you understand what the models do well enough to reason about failure modes, not just call the API
- Fluent English, written and spoken
Nice To Have:
- Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution
- Real depth in one of our blueprint industries: financial services, insurance, healthcare, asset management
- Consulting, professional services, or other embedded customer-facing delivery
- Data platform depth: data lakes, warehouses, streaming and real-time analytics, data mesh and data contracts, governance and data quality
- MLOps and classical ML: PyTorch, SageMaker, MLflow
- Fine-tuning, distillation, or inference/serving optimization
- Graph databases (Neo4j, AWS Neptune)
- IaC depth: AWS CDK, CloudFormation, Terraform
- Open-source contributions or public writing on applied AI
What you’ll do:
- Do the operator’s job for two to four weeks at the start of an engagement. Learn the function from inside, not from a requirements doc.
- Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow — in weeks, not quarters.
- Sit with the operator and the Forward Deployed Executive and redesign the function from first principles. Discovery, user research, and PRD-writing collapse into one team that re-imagines its own job. You are all three roles.
- Ship production GenAI systems into the customer’s environment — LLM applications, agentic workflows, retrieval and structured-extraction pipelines, and the services around them. Running software, not recommendations.
- Build the evaluation harness before you build the feature. When the engagement is bound to a business KPI, “it looked good in testing” is not an answer. Define what working means, instrument it, and let the evals drive the design.
- Write production code across the stack — backend services, data pipelines, and the AI layer. Python and TypeScript are our centre of gravity; we choose tools to fit the customer, not the résumé.
- Take systems to production on AWS (GCP/Azure where the customer requires it): containerized, observable, and maintainable after we leave.
- Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from.
- Work in a pair with a Forward Deployed Executive who carries the Business Unit’s KPIs. Your work is measured against the same number.
- Drive adoption. A system the BU routes around has not shipped. Change management is part of the engineering job here, not a phase after it.
- Be credible with the customer’s engineers, their operators, and their executives — and be willing to disagree with all three.
- Shape what we commit to before we commit to it. You’ll have the standing to do it, because you’re the one who will build it.
What We Offer:
- Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare
- The chance to shape how leading enterprises adopt AI, from strategy through first deployment
- A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers
- A growing AI delivery practice where you help build the tooling and frameworks, not just use them
- Remote-friendly culture
- High-impact role with direct visibility to leadership
- Strong earning potential with performance-based bonuses
- Opportunity to work with cutting-edge AI and cloud solutions
- B2B contract model
- PTO policy, paid local public holidays
- Medical insurance coverage
- Generous budget for educational opportunities
How We Hire:
- Intro conversation — the role, your background, what you want to be doing
- Two live engineering sessions. Real problems, your own editor. You may use an LLM assistant (ChatGPT, Claude) — how you work now includes these tools. Autocomplete/agentic coding tools are off for these sessions
- The redesign session. We hand you an unfamiliar business function and the constraints of the person who performs it. You have to understand the job well enough to rebuild it — then say what you’d build and how you’d know it worked. No LLMs for this one
- Team and practice conversation
