Senior AI/ML Engineer

Primary Duties and Responsibilities

• Design, build, test, deploy, and support AI-enabled applications, agents, copilots, automations, and integrations.
• Evaluate market and software trends to recommend useful AI solutions to IT leadership.
• Recommend software platforms and solution approaches that align with business and technology needs.
• Help shape organization-wide AI strategy and data practices for management, governance, curation, and AI readiness.
• Convert defined business and IT use cases into practical, production-ready AI solutions that deliver measurable business value through automation, improved decision support, increased productivity, and operational efficiency.
• Partner with stakeholders to define success criteria and measure business impact of deployed AI solutions.
• Develop agents that securely use approved enterprise content, data sources, APIs, and workflows to answer questions or complete tasks.
• Prepare enterprise documents and data for AI use through metadata, indexing, retrieval design, validation, and content quality improvements.
• Build reusable prompts, skills, connectors, templates, and technical patterns that accelerate future AI solution delivery.
• Integrate AI solutions with enterprise systems, collaboration tools, identity services, approved data platforms, and cloud-native services across Azure, AWS, or similar environments.
• Apply approved standards for security, privacy, PHI/PII protection, access control, monitoring, and responsible AI use.
• Monitor, troubleshoot, improve, and document deployed AI solutions so they remain reliable, supportable, and aligned with user needs.

Benefits and Compensation

• Choose the medical, dental, vision, and well-being benefits that best fit your needs from day one.
• 8% employer contribution to retirement plan with an additional match up to 2% on day one.
• 22 days of PTO + 12 paid holidays.
• Hybrid working arrangement.
• The compensation range for this position is $162,000 – $202,500 with a bonus opportunity and customary benefits. Final compensation will be set based on the hired applicant's qualifications (education, training, and/or experience related to this role), and as such, may fall outside the range shown.

Minimum Requirements/Qualifications

• 7+ years of professional software development experience, including 2+ years of designing, developing, or deploying AI-enabled solutions in enterprise environments.
• Experience delivering AI solutions that demonstrate measurable business outcomes such as productivity gains, process automation, knowledge discovery, service improvements, or cost reduction.
• Proficient in Python and commonly used AI/ML technologies.
• Experience designing, testing, deploying, and supporting production software solutions in enterprise environments.
• Working knowledge of modern AI and language model concepts and experience building applications with LLM APIs, prompt/context engineering, RAG, embeddings, vector search, and model evaluation.
• Understanding of agentic AI concepts including task planning, human-in-the-loop review, orchestration, guardrails, and evaluation of agent behavior.
• Experience working with relational, document, or enterprise knowledge sources such as SQL Server, Oracle, Microsoft Fabric, SharePoint, or similar.
• Strong analytical, troubleshooting, documentation, and problem-solving skills.

Preferred Qualifications

Experience with or exposure to:

• AI engineering tools such as GitHub Copilot, Claude Code, prompt management frameworks, evaluation tools, and AI operational tooling.
• AI platforms such as Azure AI Foundry, Azure AI Search, Copilot Studio, Amazon Bedrock, Amazon SageMaker, Amazon Q, OpenSearch, Pinecone, pgvector, or similar technologies supporting retrieval, semantic search, and AI application development.
• Applied machine learning and natural language processing techniques such as classification, entity extraction, semantic search, and model evaluation.
• Enterprise content preparation for AI, including document ingestion, metadata, embeddings, vector databases and semantic indexes, semantic search, retrieval validation, grounding strategies, and content lifecycle practices.
• Responsible AI, data governance, PHI/PII protection, access control, monitoring, secure development, and production support practices.
• Enterprise integration patterns, APIs, AWS Lambda-based AI integrations, Azure Functions, containers, event-driven workflows, automation tools, and human-in-the-loop review processes.