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Provectus Logo Provectus · IT - Software Development

Middle AI/ML Engineer

Colombia, Medellín · Job Posted April 11, 2026
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Job Description

As a Middle ML Engineer at Provectus, you will design, build, and deploy production ML solutions for our clients — working independently on most tasks while growing toward senior technical ownership. You'll use AI coding tools daily, mentor junior engineers, and contribute to Provectus's internal AI toolkit.

Job Responsibility

  • Build & Ship ML (55%): Design and deliver ML pipelines from experimentation to production
  • Build and optimize models — supervised, unsupervised, and generative AI
  • Write clean, tested, modular Python code
  • Deploy and monitor models
  • track performance and prevent drift
  • Contribute to LLM applications: RAG systems and agent workflows
  • Use AI coding tools on every task to move faster and write better code
  • Agentic & AI-Assisted Engineering (20%): Use Claude Code or similar AI tools to deliver client projects
  • Build with agent frameworks (Bedrock AgentCore, Strands, CrewAI, or similar)
  • Integrate or build MCP servers for internal and client use
  • Contribute features, bug fixes, or docs to the Provectus AI toolkit
  • Collaborate & Mentor (15%): Mentor junior engineers and give actionable code review feedback
  • Work closely with DevOps, Data Engineering, and Solutions Architects
  • Share knowledge through docs, presentations, or internal workshops
  • Learn & Innovate (10%): Stay current with ML research, GenAI, and agentic frameworks
  • Propose process improvements and reusable ML accelerators
  • Participate in architectural design and trade-off discussions

Requirements

  • Solid grasp of supervised/unsupervised ML: algorithms, evaluation, trade-offs
  • Deep learning hands-on experience: CNNs, RNNs, Transformers — training and fine-tuning
  • Depth in at least one domain: NLP, Computer Vision, Recommendation, or Time Series
  • Experience building LLM apps with OpenAI, Anthropic, or Hugging Face APIs
  • Hands-on RAG design: chunking, embedding, retrieval, generation
  • Familiarity with vector databases (OpenSearch, Pinecone, Chroma, FAISS)
  • Understanding of prompt engineering and LLM evaluation
  • Proficient with AI coding tools (Claude Code, Cursor, Copilot, etc.) — beyond autocomplete
  • Experience building tool-using, stateful agents with an orchestration framework
  • Understanding of Model Context Protocol (MCP) — consume or build MCP servers
  • Can write technical specs for AI execution and review/correct AI-generated output
  • Aware of agent monitoring, evaluation, and cost optimization in production
  • Solid AWS: SageMaker, Lambda, S3, ECR, ECS, API Gateway
  • Familiarity with Amazon Bedrock (model invocation, Knowledge Bases, Agents)
  • Basic awareness of Infrastructure as Code (Terraform or CloudFormation)
  • Production ML deployment experience
  • Experiment tracking with MLflow, W&B, or similar
  • CI/CD pipelines for ML
  • model monitoring and drift detection
  • Advanced Python (async/await, OOP, packaging)
  • strong pandas, NumPy, SQL
  • Docker for containerized ML workloads
  • 1–3 years of hands-on ML engineering experience
  • At least one ML model deployed to production (or near-production)
  • Team-based or client-facing project experience
  • Demonstrated use of AI-assisted development tools
  • Education: Bachelor's/Master's in CS, Data Science, Math, or equivalent practical experience
  • Strong problem-solver — breaks complexity into testable pieces
  • Clear communicator — written docs, PRs, and explanations to non-technical stakeholders
  • Fluent English (B2+)
  • Proactive — raises blockers early and comes with proposed solutions
  • Collaborative mentor who helps without creating dependency

Nice to have

  • AWS certifications
  • Kubernetes experience
  • GraphRAG or custom MCP server experience
  • Open-source contributions or published work on agentic systems

What we offer

  • Competitive salary based on competencies and market rates
  • Premium AI tooling: Claude Code, Cursor, and Provectus AI toolkit
  • Mentorship from Senior ML Engineers and Tech Leads
  • Clear growth path: Mid-Level → Senior ML Engineer → Tech Lead
  • Learning budget for courses, certifications, and conferences
  • Remote-first culture
  • work on projects across LATAM, North America, and Europe
  • Health benefits
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