Middle AI/ ML Engineer

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. Role Purpose 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.     Core Responsibilities: 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: What You'll Get: