AI/ML Engineer Team Lead

Vizient•Chicago, IL

1d•$117,600 - $206,000

About The Position

Lead the technical design, development, deployment, and operation of enterprise-grade AI solutions for complex, knowledge-intensive workflows. The role combines generative AI, agentic workflows, retrieval, structured and unstructured data, deterministic processing, and human expertise to build accurate, secure, explainable, and reliable production applications. The position remains hands-on with architecture and implementation while leading engineers, establishing evaluation and observability practices, and partnering with domain experts, product leaders, architects, data teams, and platform teams to turn ambiguous business challenges into scalable technical solutions.

Requirements

At least 7 years of relevant experience.
Experience providing technical leadership for complex, production-grade software or AI systems.
Demonstrated personal experience designing, building, deploying, and operating generative AI or machine learning applications in enterprise production environments.
Strong experience with Python and modern API and service development, including FastAPI or comparable frameworks.
Experience with large language models, prompt engineering, structured outputs, tool or function calling, retrieval-augmented generation, and agentic workflows.
Experience establishing evaluation strategies for nondeterministic AI systems, including test datasets, regression evaluation, grounding, quality measurement, and failure analysis.
Strong understanding of the appropriate boundaries between probabilistic AI behavior and deterministic software, rules, retrieval, and data processing.
Experience with cloud-native architecture, asynchronous processing, APIs, events or queues, persistence, monitoring, and production observability.
Experience addressing enterprise security, privacy, reliability, auditability, supportability, performance, and cost requirements.
Strong technical leadership, mentoring, communication, problem-solving, architecture, and cross-functional collaboration skills.

Nice To Haves

Relevant degree preferred.
Experience with Microsoft Azure, Azure AI Foundry, Azure AI Search, or comparable cloud AI and semantic-retrieval technologies.
Experience with Databricks, vector databases, embeddings, reranking, semantic search, or enterprise knowledge-retrieval platforms.
Experience developing AI solutions involving sensitive, regulated, or high-consequence data and decisions.

Responsibilities

Lead the architecture, development, deployment, and operation of enterprise-grade AI and machine learning solutions.
Design solutions combining large language models, agentic workflows, retrieval, structured data, APIs, and deterministic processing.
Translate complex expert workflows into modular, testable, and maintainable software components.
Determine when AI inference, deterministic logic, retrieval, rules, or traditional data processing will provide the most reliable outcome.
Establish evaluation strategies using representative test sets, expert-reviewed outcomes, regression testing, quality measurements, and stage-level analysis.
Design observability capabilities to capture intermediate outputs, decision traces, evaluation results, performance measures, and failure modes.
Lead investigations into grounding failures, hallucinations, retrieval quality, prompt behavior, model selection, orchestration, latency, accuracy, and cost.
Evaluate architectural tradeoffs in performance, maintainability, extensibility, security, reliability, supportability, and operating cost.
Develop APIs, services, prototypes, and production components; contribute to design reviews, code reviews, and complex technical problem-solving.
Implement security, privacy, auditability, monitoring, and production-readiness practices for AI-enabled applications.
Partner with domain experts, product leaders, architects, data teams, and platform teams to convert specialized knowledge into scalable system behavior.
Develop reusable AI capabilities, engineering patterns, evaluation frameworks, and platform components for multiple solutions.
Mentor engineers and strengthen team practices in AI engineering, software design, testing, observability, and production operations.
Drive continuous improvement using production metrics, evaluation results, expert feedback, and real-world user outcomes.