Responsibilities:

Take the seat

• Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles.

• Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow.

Build

• Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases

• Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design.

• Contribute code to critical or complex components

• Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. 

• Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client.

• Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from.

• Lead architecture reviews, produce technical design documents, and contribute to standards. 

Mentorship & Team Development

• Mentor junior and mid-level ML engineers (2-5 engineers)

• Conduct technical code reviews

• Provide guidance on technical problem-solving

• Create learning opportunities and growth paths, share knowledge

• Build technical competency across the team

Own the outcome. 

• Work with FDX, AI Engineers.

• Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here.

• Be credible with the customer’s engineers and their executives. 

• Shape what we commit to before we commit to it. 

Requirements:

Mindset

Proactive and self-directed; identify problems before they're handed to you

Comfort with ambiguity and ownership.

B2+ English, comfortable collaborating across distributed, multicultural teams

Client Engagement

You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code

Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them

You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run

Technical depth

Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes

Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks

Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure 

Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.

Experience building and optimizing RAG systems in production

Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. 

Experience in making and defending architectural trade-off decisions

Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus

Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines

You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release

Model and agent monitoring, drift detection 

Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs

Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus 

MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus

Team Leadership

Experience mentoring the team

Experience in creating growth paths and technical competency across the team

Nice to Have:

• Experience in one of the industries: financial services, insurance, healthcare

• Consulting, professional services, or other embedded customer-facing delivery

• A2A: you can explain agent-to-agent interoperability 

• AWS and Claude Code Certifications

• CI/CD pipeline experience (GitHub Actions, GitLab CI)

• Experience in an additional language (Go, TypeScript, or Rust)

• Experience with Apache Spark, Apache Airflow, Kafkа

What We Offer:

The chance to shape how leading enterprises across LATAM, Europe, and North America 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

Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance

Career growth; we actively develop our engineers

Access to the latest AI tools and premium subscriptions

Long-term B2B collaboration

Private medical insurance or a budget for your medical needs

Paid sick leave, vacation, and public holidays

Equipment and all the tech you need for comfortable, productive work

How we hire:

1. Intro conversation. The role, your background and aspirations, tech questions.

2. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant 

3. HM interview. Tech questions; a live engineering session is also possible