Middle AI/ML Engineer (GenAI, AWS) at 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.   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.