AI Engineering Lead

Ochsner Clinic Foundation•New Orleans, LA

About The Position

The Lead AI Engineer leads the design, engineering, delivery, and operation of the machine-learning and conversational AI platform that powers automated patient and customer interactions. Defines AI/ML engineering strategy, standards, and roadmaps, and directs the teams that build data and feature pipelines, train and evaluate models, and productionize conversational AI behind reliable, secure, and cost-efficient services. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential duties. This job description is a summary of the primary duties and responsibilities of the job and position. It is not intended to be a comprehensive or all-inclusive listing of duties and responsibilities. Contents are subject to change at the company’s discretion.

Requirements

Bachelor's Computer Science, Machine Learning, Data Science, or related field; equivalent experience may be considered in lieu of a degree.
8 years Software/ML engineering experience, including hands-on experience productionizing machine-learning or conversational AI systems with a minimum of 3 years in technical leadership or team-lead capacity
Deep knowledge of machine learning, natural language processing, and conversational AI (including retrieval-augmented generation, embeddings, and large, language model-based systems); strong experience with cloud platforms, MLOps tooling, containers, and CI/CD.
Strategic planning and roadmap translation into engineering execution.
System design for scalability, performance, and reliability.
Decision-making under ambiguity in emerging AI technologies.
Translates technical concepts into business language for non-technical executives
Strong stakeholder management and cross-functional influence
High ownership; operates well with ambiguity and shifting priorities
Committed to developing people and building a durable team
Sound judgment on when to ship, when to iterate, and when to stop

Nice To Haves

Master's
Experience I healthcare or another regulated, HIPAA-relevant environment strongly preferred.
Experience with LLM and generative AI applications — RAG, prompting, evaluation, and fine-tuning
Experience designing and operating systems in AWS (SageMaker, Bedrock) or a comparable cloud
Strong SQL and experience with modern data platforms (Snowflake, Databricks, or similar)
Experience with MLOps tooling, CI/CD, and infrastructure automation
Demonstrated track record of quantifying business impact from AI investments

Responsibilities

Establishes AI/ML engineering vision, operating model, and technical standards (coding, testing, security, observability) for the conversational AI platform.
Translates product and business roadmaps into multi-quarter engineering plans, milestones, staffing models, and budgets.
Directs development across data ingestion, feature computation, labeling, training, evaluation, packaging, deployment, and inference.
Implements model lifecycle management and governance, including reproducibility, experiment tracking, versioning, approvals, and rollback.
Builds and scales feature stores, model registries, and training/inference platforms, optimizing for cost, latency, throughput, and reliability service-level agreements (SLAs).
Collaborates with Product, Risk, Legal, and Compliance to document model intent, limitations, and human-in-the-loop mechanisms, and to prepare artifacts for audit in a HIPAA-regulated setting.
Leads incident response for model and service regressions, and mentor staff and develop hiring and career paths for AI/ML engineering roles.
Performs other duties as assigned
Complies with all policies and standards