Machine Learning Engineer Senior

Leads in the development of cross-functional, multi-platform machine learning software systems. Utilizes technical knowledge and industry experience to design, build and maintain technology solutions. Applies complex higher level math concepts to build new models and validate existing models.

What You Will Do

Performs complex application programming activities including, coding, testing, debugging, documenting, maintaining, and modifying machine learning systems. Works with Data Scientist to understand prototype, and suggests modifications as necessary.

Why It Might Be a Fit

Possesses Machine Learning industry expertise and stays current on best practices, hot topics, and emerging trends. Utilizes industry expertise to inform decisions and recommendations.

Requirements

• 5-7 years related experience
• Experience implementing production solutions that leverage generative AI and LLMs
• Implementation/use of SDLC harnesses for increasing personal and team productivity
• Model solution design, build, orchestration and implementation
• Experience building solutions with model governance, risk management, and regulatory adherence at top of mind
• Continuous support of models deployed to production applications
• Release planning, coordination, and hands on support throughout release windows
• Machine Learning / GenAI
• Python, scikit-learn, PyTorch and/or TensorFlow
• Generative AI and Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG), embeddings, vector search
• Agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, or similar
• Model evaluation, validation, prompt engineering, and model monitoring
• Advanced probability, statistics, linear algebra, and optimization
• AI/ML Platforms
• Azure Machine Learning, Azure AI Foundry, AWS SageMaker, or equivalent enterprise ML platforms
• Azure OpenAI/OpenAI APIs or comparable foundation-model platforms
• MLflow or similar experiment tracking/model lifecycle tooling
• Vector databases/search platforms such as Azure AI Search, Pinecone, Weaviate, or pgvector
• Development & Engineering
• Git/GitHub or Azure DevOps
• REST APIs and microservices
• Docker and containerized deployments
• CI/CD pipelines and automated testing
• Jira/Confluence or similar Agile delivery tools
• SDLC practices, source control, code reviews, release management, and production support
• Data
• SQL
• Data pipelines and data preparation
• Structured and unstructured data
• Feature engineering
• Data quality and validation
• API and enterprise data integration
• Governance / Risk Knowledge
• Model governance and model risk management
• Responsible AI concepts
• Explainability, validation, bias/fairness, data privacy, and security
• Auditability and documentation
• Regulatory adherence and production controls