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Medellín, Antioquia, Colombia
Remote only
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.
You will work in a small, senior pod alongside an FDX, and AI Engineers.
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.
Tech lead embeds with a client, maps the client workflow as it actually happens, identifies the business problem underneath it, builds a working AI solution, presents to the client in the language of outcomes, and transfers the knowledge to the client. Setting technical competency across the team and mentoring junior and mid-level AI engineers.
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.
Contribute code to critical or complex components
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.
Mentorship & Team Development
Mentor junior and mid-level ML engineers (2-5 engineers)
Conduct technical code reviews
Provide guidance on technical problem-solving
Create learning opportunities and growth paths, share knowledge
Build technical competency across the team
Own the outcome.
Work with FDX, AI Engineers.
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.
Mindset
Proactive and self-directed; identify problems before they're handed to you
Comfort with ambiguity and ownership.
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
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.
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
Team Leadership
Experience mentoring the team
Experience in creating growth paths and technical competency across the team
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
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
HM interview. Tech questions; a live engineering session is also possible
