Middle AI/ML Engineer (GenAI, AWS)

Provectus is an AWS Premier Consulting Partner and AI consultancy featured in Forrester's AI Technical Services Landscape, with 15+ years of experience and 400+ engineers. We build production AI for global enterprises in partnership with Anthropic, Cohere, and AWS.

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.

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.