About the role
Role Description:
Leads in the development of cross-functional, multi-platform machine learning software systems. Uses technical knowledge and industry experience to design, build and maintain technology solutions. Utilizes knowledge of advanced probability and statistics to create intricate machine learning algorithms. Applies complex higher level math concepts to build new models and validate existing models. Possesses Machine Learning industry expertise and stays current on best practices, hot topics, and emerging trends. Utilizes industry expertise to inform decisions and recommendations. 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. Easily understands the bigger picture and possesses an enterprise-wide view. Skills: Machine Learning Customer Solutions Risk Assessments Technical Knowledge Competitive Advantages Design Enterprise Architecture Framework Experience: 5-7 years related experience.
* Experience implementing production solutions that leverage generative AI and LLMs, agentic frameworks * Implementation/use of SDLC harnesses for increasing personal and team productivity * Internal client consultation to elicit and collect requirements, and communicate and align the technical team toward delivery * 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