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:
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.
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.
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.
Take the seat
Build
Own the outcome.
What You’ll Bring:
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
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
Mindset
Client Engagement
Technical depth
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
Skills Required
- 7+ years building and running production systems
- B2+ English proficiency and ability to collaborate across distributed multicultural teams
- Experience designing and shipping production LLM applications and agentic workflows
- Solid AI and machine learning foundations, including reasoning about model failure modes
- Experience with multi-step agentic workflows, graph-based orchestration, tool use, state management, and partial-failure recovery
- Experience with LLM APIs such as Anthropic, AWS Bedrock, or OpenAI and agent frameworks
- Production experience building and optimizing retrieval-augmented generation systems
- Strong engineering fundamentals and proficiency in Python and/or TypeScript
- Experience making and defending architectural trade-off decisions
- Hands-on AWS production experience with services such as Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, and ECR
- Experience with containers, ECS or Kubernetes, infrastructure as code, and CI/CD for AI pipelines
- Experience building or owning evaluation suites for nondeterministic systems, including ground truth and release gates
- Experience with model and agent monitoring and drift detection
- Ability to manage model costs and latency using tiering and caching
- Hands-on production experience with Claude Code, CLAUDE.md, hooks, and skills files
- Understanding of MCP and when to use it instead of REST integrations
- Ability to understand client workflows, engage senior stakeholders, and present business outcomes
- Ability to create phased delivery plans with deliverables, dependencies, risks, and cost estimates
- Founder, CTO, or engineering leadership experience
- Experience in financial services, insurance, or healthcare
- Consulting, professional services, or embedded customer-facing delivery experience
- Ability to explain agent-to-agent interoperability
- AWS and Claude Code certifications
- CI/CD experience with GitHub Actions or GitLab CI
- Experience with Go, TypeScript, or Rust
- Experience with Apache Spark, Apache Airflow, or Apache Kafka
What We Do
Provectus is an Artificial Intelligence consultancy and solutions provider, helping businesses achieve their objectives through AI. We are recognized by industry think tanks as a leading provider of AI solutions in specific business domains, driven by sophisticated IT service management and tech innovation. Provectus is a value driver and a trusted partner for our clients and employees. Provectus is an AWS Premier Consulting Partner with competencies in Data & Analytics, DevOps, and Machine Learning. We design and build AI solutions for industry-specific use cases, Data and Machine Learning foundation, Cloud transformation, and DevOps adoption.
