Middle AI/ML Engineer (GenAI, AWS)

What You'll Do:

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.

What You Need:

Machine Learning

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.

LLMs & Generative AI

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.

Agentic Engineering (Required)

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.

Cloud & Infrastructure

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).

MLOps & Data

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.

Experience & Education

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.

Key Traits

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.