Sr. AI Delivery & Operations Engineer

Vizient•Chicago, IL

About The Position

In this role, you will serve as the technical anchor for a focused AI delivery squad, leading the end-to-end engineering, deployment, and operational support of enterprise AI solutions. You will design, build, and support AI-powered applications, intelligent automation solutions, agentic workflows, and reusable AI engineering capabilities that accelerate Vizient's AI Factory strategy. Working closely with software engineers, AI architects, product leaders, and business stakeholders, you will establish engineering standards, delivery patterns, and operational best practices that enable secure, scalable, and reliable AI solutions across the enterprise. This role combines hands-on software engineering, AI application development, AI operations, and platform engineering within a governance-first healthcare environment.

Requirements

Bachelor's degree or equivalent combination of education and relevant experience preferred.
5+ years of professional software engineering experience with strong expertise in application architecture, API development, testing, source control, CI/CD, deployment automation, and modern software engineering practices required.
Hands-on experience designing, developing, deploying, and supporting production AI applications, including post-deployment monitoring, troubleshooting, optimization, and operational support required.
Strong proficiency in Python and experience building enterprise-grade software solutions using modern development frameworks and engineering best practices required.
Experience developing AI solutions utilizing large language models (LLMs) and platforms such as OpenAI, Azure OpenAI, Anthropic, Gemini, or comparable AI technologies required.
Experience with cloud-native application development, containerization, infrastructure automation, observability platforms, logging, monitoring, and production support within enterprise environments required.
Strong analytical, problem-solving, communication, and cross-functional collaboration skills with the ability to thrive in fast-paced, highly collaborative, and evolving technical environments required.

Nice To Haves

Knowledge of AI engineering concepts including Retrieval-Augmented Generation (RAG), agentic AI, prompt engineering, evaluation frameworks, guardrails, and AI quality engineering preferred.
Experience working within regulated environments supporting data governance, privacy, security, and compliance, preferably within healthcare or similarly regulated industries, preferred.

Responsibilities

Design, develop, test, deploy, and support enterprise AI applications, agentic workflows, retrieval-augmented generation (RAG) solutions, and model-powered APIs throughout the full AI delivery lifecycle.
Serve as the primary technical contributor for an AI delivery squad, providing technical leadership while driving high-quality, scalable, and maintainable AI solutions.
Build and enhance reusable AI Factory capabilities, including shared services, engineering frameworks, implementation patterns, and platform components that accelerate enterprise AI adoption.
Develop and maintain AI quality engineering capabilities, including automated testing, evaluation frameworks, guardrails, structured outputs, drift detection, failure analysis, and continuous performance optimization.
Support AI Operations (AIOps) and LLMOps through implementation of observability, telemetry, logging, monitoring, incident response, root cause analysis, production support, and operational excellence practices.
Contribute to deployment automation, CI/CD pipelines, release management, infrastructure automation, and operational processes that enable secure and reliable AI solution delivery.
Collaborate with internal teams and external implementation partners to evaluate technical designs, establish engineering standards, and expand internal AI engineering capabilities through knowledge sharing and mentoring.
Support synthetic data initiatives and AI solutions operating within regulated healthcare environments by implementing secure, compliant, auditable, and governance-first engineering practices.