Provectus - Middle AI/ML Engineer (GenAI, AWS)
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Requirements
• 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. • 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). • 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 (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. • 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. • AWS certifications; • GraphRAG or custom MCP server experience • Open-source contributions or published work on agentic systems.
Responsibilities
• 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; • 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.
Benefits
• 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; • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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