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