Middle AI/ML Engineer (GenAI, AWS)

About Provectus

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.

The Role

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.