Mid-Level ML Engineer at Provectus

<div> <h3>Role Purpose</h3> <p style="text-align: justify;">As a Mid-Level ML Engineer at Provectus, you will work with increasing independence to design, implement, and deploy production-grade ML solutions for our clients. You sit at the bridge between learning and leading: you no longer require task-by-task guidance, yet you continue to grow toward senior technical ownership. A defining characteristic of this role is proficiency in AI-assisted development. You will leverage AI coding tools, contribute to agentic engineering initiatives, and actively shape Provectus's internal AI toolkit. You will also mentor junior engineers and contribute meaningfully to technical design decisions.</p> <p>&nbsp;</p> </div> Core Responsibilities Technical Delivery (55%) • Design and implement ML pipelines from experimentation to production with limited supervision • Build, evaluate, and optimize models across supervised, unsupervised, and generative AI tasks • Develop and maintain production-grade Python code: modular, tested, and well-documented • Set up reproducible experimentation environments and maintain experiment pipelines • Deploy and monitor ML models in production, ensuring stability and performance • Actively contribute to LLM-based applications, including RAG systems and agent workflows • Leverage AI-assisted development tools to increase velocity and code quality on all tasks Agentic Engineering & AI-Assisted Development (20%) • Claude Ecosystem Integration: practical use of Claude Code or the Claude Agent SDK to deliver high-quality greenfield customer engagements • Transform existing brownfield projects into AI-friendly setups • Active usage of the Provectus AI toolkit in daily workflows • Internal Contributions: contribute back to the Provectus AI toolkit, developing specific agents, building MCP servers, submitting bug fixes, adding features, or improving documentation • Agent Frameworks: hands-on experience with Amazon Bedrock AgentCore, Strands, CrewAI, or equivalent orchestration frameworks for building tool-using and multi-step agentic systems • MCP Integration: understanding of Model Context Protocol and ability to integrate or build MCP servers for client or internal use • Stay current with emerging AI coding tools and agentic frameworks, sharing relevant findings with the team Collaboration and Contribution (15%) • Mentor and support junior ML engineers on technical tasks, code quality, and best practices • Conduct meaningful code reviews with constructive, actionable feedback • Collaborate with cross-functional teams: DevOps, Data Engineering, Solutions Architects • Share knowledge through documentation, presentations, and internal workshops on AI tooling Innovation and Growth (10%) • Stay current with ML research and emerging frameworks, especially in GenAI and agentic AI • Propose improvements to existing solutions, pipelines, and team processes • Contribute to the development of reusable ML accelerators and internal quick-starts • Participate in technical design discussions and architectural trade-off conversations Technical Requirements Machine Learning Core • Strong grasp of supervised and unsupervised ML: algorithms, evaluation, and real-world trade-offs • Practical experience with classification, regression, and feature engineering in production or near-production contexts • Hands-on experience with deep learning: CNNs, RNNs, Transformers training and fine-tuning • Solid understanding of model evaluation, bias-variance trade-offs, and validation strategies • Experience with at least one ML domain in depth: NLP, Computer Vision, Recommendation, or Time Series LLMs and Generative AI • Practical experience building LLM-based applications using OpenAI, Anthropic, or Hugging Face APIs • Hands-on experience designing and implementing RAG systems (chunking, embedding, retrieval, generation) • Working knowledge of vector databases (OpenSearch, Pinecone, Chroma, FAISS) and embedding models • Understanding of prompt engineering, chain-of-thought reasoning, and LLM evaluation techniques • Awareness of Amazon Bedrock capabilities: model invocation, Knowledge Bases, and Agent capabilities Agentic Engineering & AI-Assisted Development • AI-Assisted Development: demonstrated proficiency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) not just autocomplete, but strategic use for generation, refactoring, debugging, and documentation • Agent Frameworks: hands-on experience with Amazon Bedrock AgentCore, Strands, CrewAI, or similar orchestration frameworks; ability to build stateful, tool-using agents • MCP Integration: working understanding of Model Context Protocol; ability to consume or contribute to MCP servers for internal or client-facing integrations • Tool Use & Function Calling: practical experience implementing tool-using agents with proper error handling, fallbacks, and state management • Spec-Driven Development: ability to write clear technical specifications that AI tools can execute effectively, reviewing and correcting AI-generated output • AgentOps Awareness: understanding of agent monitoring, evaluation, and cost optimization patterns in production Cloud and Infrastructure • Solid AWS experience with core ML services: SageMaker, Lambda, S3, ECR, ECS, API Gateway • Familiarity with Amazon Bedrock: model invocation, Knowledge Bases, and Agent capabilities • Understanding of cloud-native ML architectures and serverless patterns • Awareness of Infrastructure as Code (Terraform, CloudFormation) at a conceptual or hands-on level MLOps and Production • Practical experience deploying ML models to production environments • Experience with experiment tracking: MLflow, Weights & Biases, or equivalent • Working knowledge of CI/CD pipelines for ML (GitHub Actions, Jenkins, or similar) • Model monitoring: tracking performance degradation, drift detection, and alerting • Familiarity with orchestration tools: Airflow, Prefect, or Step Functions Data and Programming • Advanced Python proficiency: async/await patterns, OOP, modular code, packaging • Expert-level pandas and NumPy; familiarity with Spark or Dask for larger data sets • Strong SQL: complex queries, window functions, optimization basics • Docker: building, running, and debugging containerized ML workloads Nice-to-Have Technical Skills • AWS Certifications (Cloud Practitioner, Solutions Architect Associate, or ML Specialty) • Experience with Kubernetes or container orchestration beyond Docker Compose • GraphRAG implementation experience • Experience building custom MCP servers • Contributions to open-source ML projects or AI toolkit repositories Core Competencies Problem-Solving • Breaks down complex ML problems into well-scoped, testable components • Makes sound technical decisions with moderate uncertainty and available data • Proactively identifies and addresses technical debt before it becomes critical • Considers operational constraints: cost, latency, reliability, and maintainability Communication • Clear technical writing for documentation, design docs, and pull requests • Able to explain ML concepts to non-technical stakeholders at an appropriate level • Effective in distributed, async team environments with global collaborators • Fluent English (B2+ written and verbal) Professional Excellence • Delivers assigned components with minimal supervision and consistent quality • Proactively raises blockers and proposes solutions rather than waiting for direction • Maintains high code quality standards, including testing and documentation • Self-directed learner who tracks ML and AI tooling advancements Collaboration and Emerging Mentorship • Provides helpful, specific feedback in code reviews • Supports junior engineers without blocking their growth or creating dependency • Contributes positively to team culture and knowledge sharing on AI tooling • Approaches disagreements with data and reasoning, not authority Experience and Education Required • Demonstrated competency equivalent to 1-3 years of hands-on ML engineering experience • Track record of deploying at least one ML model to a production or production-like environment • Experience working on team-based or client-facing projects (not solely academic or solo) • Demonstrated proficiency with AI-assisted development tools and agentic frameworks Education (one of) • Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Engineering, or related field • Equivalent self-taught expertise with a demonstrable production or near-production project history • Bootcamp or certification with significant practical ML engineering experience Nice-to-Have • Experience working in consulting or client-facing environments • Previous experience in distributed or remote international teams • Contributions to technical blogs, conference talks, or open-source • Published work on agentic systems or AI tooling