Time since we first saw it
Day 14
Similar jobs usually come down between day 4 and day 77.
At Provectus, jobs usually come down by about day 35.
From 51 similar postings that came down, and 12 of Provectus’s. Coming down is not always a hire.
FDE AI/ Solutions Architect (AI, Python/Data)
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: remote
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingMap the client's workflow as it actually happens and identify the business problem beneath it
Design and ship production GenAI, LLM and agentic systems into the client's environment
Implement and optimize RAG systems for production use cases
Build the evaluation harness before the feature and let evals drive the design
Take systems to production on AWS with CI/CD, testing and monitoring, then hand them over
From the employer’s posting
map the client workflow as it actually happens, identify the business problem underneath it
Design and ship production GenAI systems into the customer’s environment
Implement and optimize RAG systems for production use cases
Build the evaluation harness before you build the feature.
Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring
Benefits in the posting
Full benefits wording- Remote-friendly culture
- Access to the latest AI tools and premium subscriptions
- Private medical insurance or a budget for your medical needs
- Paid sick leave, vacation, and public holidays
- How we hire:
- Employment type
- Full-time
From the employer’s posting.
Posting record
Report an errorLikely evergreenReposted 2 times since Jul 28, 2026. A role that keeps coming back is often kept open to collect applications rather than to fill one seat.
- Status in our records
- Live
- First seen by us
- Oct 30, 202614 days ago
- Recorded sightings
- 15
- Last seen by us
- Nov 13, 2026
- Reposted
- 2 times since Jul 28, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
How this job compares
Provectus is hiring
9 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- AI Engineer hiring is picking up
- Takes jobs down at a usual pace
- 56% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Rust
- TypeScript
- AWS
- Azure
- GCP
- Kubernetes
- S3
- Spark
- Airflow
- Go
Where the posting names them
- 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
- CI/CD pipeline experience (GitHub Actions, GitLab CI) - Experience in an additional language (Go, TypeScript, or Rust) - Experience with Apache Spark, Apache Airflow, Kafkа
- 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 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
- 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.
- 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
- 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
- A2A: you can explain agent-to-agent interoperability - AWS and Claude Code Certifications - CI/CD pipeline experience (GitHub Actions, GitLab CI)
- 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
- 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
- Experience in an additional language (Go, TypeScript, or Rust) - Experience with Apache Spark, Apache Airflow, Kafkа What We Offer:
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Has Provectus posted this job before?
Yes. We’ve followed 4 other FDE AI/ Solutions Architect (AI, Python/Data) jobs there since April 2026. One came down after 24 days. 3 are still up.
Large employers often post one title for several teams, so these can be separate openings.
Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job description
-
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.
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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:
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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.
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Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow.
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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.
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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.
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Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team.
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Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number.
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Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here.
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Be credible with the customer’s engineers and their executives.
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Shape what we commit to before we commit to it.
Take the seat
Build
Own the outcome.
What You’ll Bring:
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Proactive and self-directed; identify problems before they're handed to you
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Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job
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B2+ English, comfortable collaborating across distributed, multicultural teams
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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
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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
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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
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7+ years building and running production systems.
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Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes
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Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks
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Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure
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Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
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Experience building and optimizing RAG systems in production
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Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack.
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Experience in making and defending architectural trade-off decisions
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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
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Model and agent monitoring, drift detection
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Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs
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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
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MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus
Mindset
Client Engagement
Technical depth
Nice to have:
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Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution
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Experience in one of the industries: financial services, insurance, healthcare
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Consulting, professional services, or other embedded customer-facing delivery
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A2A: you can explain agent-to-agent interoperability
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AWS and Claude Code Certifications
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CI/CD pipeline experience (GitHub Actions, GitLab CI)
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Experience in an additional language (Go, TypeScript, or Rust)
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Experience with Apache Spark, Apache Airflow, Kafkа
What We Offer:
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The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment
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A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers
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A growing AI delivery practice where you help build the tooling and frameworks, not just use them
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Remote-friendly culture
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Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance
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Career growth; we actively develop our engineers
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Access to the latest AI tools and premium subscriptions
-
Long-term B2B collaboration
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Private medical insurance or a budget for your medical needs
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Paid sick leave, vacation, and public holidays
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Equipment and all the tech you need for comfortable, productive work
How we hire:
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Intro conversation. The role, your background and aspirations, tech questions.
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Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
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HR Interview. Soft skills and expectations
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HM interview. Tech questions; a live engineering session is also possible
Employment type
Full-time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Ukraine
- 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
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.