[Hiring] AI/Solutions Architect @Provectus

Role Description

You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize:

• Forward Deployed Executive (FDX): Owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction.
• Forward Deployed Engineer (FDE): Embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it.

Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered.

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.
• 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.
  • 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. 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.

Qualifications

• 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.

Requirements

• 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 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.

Benefits

• 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.