FDE AI/ Solutions Architect (AI, Python/Data) @ Provectus — Vaga Remota

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.

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.

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.

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.
Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack.
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.
Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs.

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.
AWS and Claude Code Certifications.
CI/CD pipeline experience (GitHub Actions, GitLab CI).

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.

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.