FDE AI/ Solutions Architect (AI, Python/Data)

What You’ll Do:

• 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.

• 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.

• 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.

• Lead architecture reviews
  Produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team.

• Own the outcome.
  Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number.

• 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.

What You’ll Bring:

Mindset

• Proactive and self-directed; identify problems before they're handed to you.
• Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job.
• 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

• 7+ years building and running production systems.
• 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. Python and/or TypeScript proficiency; depth matters more than stack.
• 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.

Nice to have:

• Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution.
• 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, Kafka.

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:

• Intro conversation. The role, your background and aspirations, tech questions.
• Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant.
• HR Interview. Soft skills and expectations.
• HM interview. Tech questions; a live engineering session is also possible.