Senior AI/ML Engineer

Wespath Benefits and Investments•Glenview, IL

5d•Hybrid

About The Position

Wespath's Information Technology team has an opportunity for a Senior AI/ML Engineer. This role is a hands-on software developer responsible for designing, building, deploying, and supporting secure AI-enabled solutions that improve business and IT productivity. This role develops AI agents, copilots, automations, integrations, and reusable components using approved enterprise data, documentation, APIs, Microsoft AI platforms, AWS AI services, and cloud-native deployment patterns. The position partners with Product Management, business stakeholders, IT teams, and governance partners to turn defined opportunities into reliable, scalable, and production-ready AI solutions that reduce manual effort, improve knowledge discovery, accelerate software delivery, enhance service quality, and increase operational efficiency.

Requirements

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.

Nice To Haves

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.

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.