FDE AI / Solutions Architect (AI, Python, Data)
Posted 2 days ago
The role in short
Embed with clients to design, build, and deploy production GenAI and agentic systems, owning the technical delivery and business outcomes.
Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.
Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.
Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.
Where this role sits
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.
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, 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:
- 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
Prep for this interview
5 questions likely to come up at Provectus, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “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”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Cloud, deployment and security
They wrote: “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.”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
- 3Discovery and scoping
They wrote: “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”
How do you decide what to build first when a customer wants ten things?
How to answer
Why they ask: FDEs have limited weeks per customer. This tests judgment about value, effort and risk.
- Score each request by business value and by effort, with the customer in the room
- Pick one item that proves value fast and unblocks later work
- Write down what's out of scope so it doesn't creep back in
- Revisit the list after the first release, with real usage data
Tip: Show a simple way to rank the list and name what you'd leave out on purpose.
- 4Coding
They wrote: “Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
- 5Ownership and working with customers
They wrote: “Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job”
Tell me about a project where the requirements kept changing. What did you do?
How to answer
Why they ask: Ambiguity is the norm. They want proof you can keep moving without a clear spec.
- The project and why requirements kept moving
- What you did to pin them down (short demos, written decisions)
- The trade-offs you made to keep shipping
- The result, with a number if you have one
Tip: Show how you reduced the change, beyond how you coped with it.
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