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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.
Job Responsibility
Build 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
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
Contribute features, bug fixes, or docs to the Provectus AI toolkit
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
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
Machine Learning
Deep learning hands-on experience: CNNs, RNNs, Transformers
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
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.)
Experience building tool-using, stateful agents with an orchestration framework
Understanding of Model Context Protocol (MCP)
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
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
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
Clear communicator
Fluent English (B2+)
Proactive
Collaborative mentor
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