Sr Mgr AI

Medline•Northbrook, IL

12d

About The Position

This strategic and hands-on practitioner will lead the delivery of high-impact AI initiatives across our global organization. This role will oversee a team of up to 10 U.S.-based AI professionals and provide dotted-line leadership to a 15-member team in India. The ideal candidate will combine deep technical expertise with strong leadership and communication skills to drive innovation, improve team capabilities, and deliver business value through AI.

Requirements

Master’s degree in Computer Science, Data Science, Engineering, or related STEM field
Any Microsoft/AWS/GCP AI and cloud related
7+ years of relevant experience in AI/ML, with 3+ years in a leadership or management role.
Realized examples going to enterprise level production with LLM and Traditional ML models and realizing measurable business impact of these solutions.
Development: AzureML Studio, Docker, Azure CLI, Visual Studio Code
Data Engineering: Azure Databricks, Azure Synapse, Azure Data Factory
Modeling & Deployment: Azure OpenAI, GenAI/LLM, Traditional Machine Learning
Dev/MLOps: Azure DevOps, GitLab, MLFlow, Prompt Flow
Inferencing: API Gateway (OpenAI/Azure AI Search), Python APIs, FastAPI

Nice To Haves

PHD in Computer Science, Data Science, Engineering, or related STEM field
Microsoft AI / cloud /architecture related
10+ years of relevant experience in AI/ML, with at least +5 years in a leadership or management role.
Realized examples going to enterprise level production with LLM and Traditional ML models and realizing measurable business impact of these solutions.
Considerable experience with various Retrieval Augmented Generation techniques (i.e. Graph RAG)
Development: AzureML Studio, Fabric Data Science Module, Docker, GitHub Copilot, Azure CLI, Visual Studio
Data Engineering: Fabric Spark/Synapse, Talend, Azure Databricks, Azure Synapse, Azure Data Factory, Power Platform, React/Next.js
Modeling & Deployment: Azure AI Foundry, Azure OpenAI, Azure ML Studio, Gen/AI LLM, Computer Vision, Traditional ML
Dev/MLOps: Azure DevOps, GitLab, JFrog, Prisma Cloud, SonarQube, MLFlow, Prompt Flow, Key Vault
Inferencing: API Gateway (OpenAI/Azure AI Search), Java/.NET/Python APIs, FastAPI, Azure APIM
Monitoring & Testing: Grafana, Selenium, Katera, Load Testing

Responsibilities

Lead the execution of prioritized AI initiatives: Oversee the planning, resourcing, and delivery of AI projects including GenAI, LLMs, and traditional ML. Define project scope, success metrics, and timelines in collaboration with business and technical stakeholders. Ensure alignment with enterprise priorities and integration with existing systems and data platforms. Manage risks, dependencies, and escalations to ensure timely and high-quality delivery.
Manage and mentor a cross-functional AI team across the U.S. and India: Provide day-to-day leadership, coaching, and performance management for a team of up to 10 direct reports and 15 dotted-line reports. Conduct regular 1:1s, team meetings, and performance reviews. Foster a culture of accountability, innovation, and continuous learning. Coordinate work across time zones and ensure effective collaboration between global teams.
Partner with stakeholders across healthcare, manufacturing, logistics, and commercial domains: Work closely with enterprise architects, data engineers, software developers, and platform teams to design and implement scalable AI solutions. Translate complex AI concepts and requirements into actionable technical plans that align with enterprise architecture and infrastructure standards. Participate in technical design reviews, architecture discussions, and solution planning sessions to ensure AI initiatives are well-integrated and future-proof. Facilitate knowledge sharing through technical demos, architecture walkthroughs, and collaborative workshops with internal and external technology teams. Align AI development with broader IT strategies, ensuring interoperability, security, and compliance across systems and platforms.
Oversee the full AI lifecycle: Guide the team through data acquisition, preprocessing, feature engineering, and model selection. Ensure robust model training, validation, and testing practices. Lead deployment strategies including containerization, API development, and integration with enterprise systems. Establish monitoring and feedback loops to track model performance and business impact.
Drive continuous improvement in team capabilities: Identify skill gaps and create development plans for team members. Promote knowledge sharing through code reviews, design sessions, and internal tech talks. Evaluate and introduce new tools, frameworks, and methodologies to improve productivity and model quality. Encourage experimentation and innovation while maintaining alignment with enterprise standards.