Senior AI/ML Engineer (GenAI, AWS)

Colombia·MedellínRemoteFull-timesenior

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

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.

Technical Tools

Machine Learning EngineerData

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 FDE and an FDX,

Proactive and self-directed; you push for clarity rather than waiting for a ticket

Excellent communication and problem-solving skills

Comfort with ambiguity and ownership.

B2+ English, comfortable collaborating across distributed, multicultural teams.

5+ years in software or ML engineering, with production systems you were accountable for.

Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes.

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. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive.

Hands-on AWS in production: 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.

Experience in one of the industries: financial services, insurance, healthcare.

Consulting, professional services, or other embedded customer-facing delivery.

AWS and Claude Code Certifications

A2A: you can explain agent-to-agent interoperability

CI/CD pipeline experience (GitHub Actions, GitLab CI)

Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.

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 FDE and FDX

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