Data Science & AI Manager

INFOSYS NOVA HOLDINGS LLCCharlotte, NC
7hHybrid

About The Position

We are seeking a Data Science & AI Manager with deep expertise in agentic AI systems, MCP (Model Context Protocol) servers, and real-time operational intelligence. This role will drive the development of autonomous and semi-autonomous AI agents, orchestrate data-powered workflows across Healthcare Digital products, and deliver measurable improvements to patient experience and associate operations.

Requirements

  • Bachelor’s degree in a relevant field or equivalent professional experience.
  • 6+ years of experience in data science, AI engineering, or applied ML, including 2+ years of team leadership or technical management.
  • Hands-on experience building agentic AI systems, including: Multi-agent workflows, Tool-using agents, Planning/monitoring agents
  • Strong experience with MCP servers or similar agent integration frameworks (e.g., LangChain tools, AutoGen, OpenAI tool calling).
  • Proficiency in Python, SQL, ML frameworks (PyTorch, TensorFlow, scikit-learn).
  • Experience with cloud data and compute platforms (Azure, Databricks, AWS, or GCP).
  • Strong understanding of LLMs, RAG pipelines, structured tool protocols, and knowledge graph integration.
  • Excellent communication, stakeholder partnership, and product-oriented thinking.

Nice To Haves

  • Experience with healthcare, foodservice, hospitality, or operational environments.
  • Familiarity with IoT data streams, workforce management systems, or real-time task operations.
  • Background in optimization, reinforcement learning, or continuous planning agents.

Responsibilities

  • Lead the strategy, architecture, and implementation of agentic AI systems for Healthcare Digital.
  • Design and manage MCP servers that provide structured, secure tool access for AI agents across platforms including meal ordering, food production, and EVS task management.
  • Build multi-agent systems with clear roles—e.g., planning agents, QA agents, data-retrieval agents, and operational copilots—that collaborate to support healthcare workflows.
  • Develop governance and routing layers that enable AI agents to safely execute tasks, call tools, generate recommendations, and interact with structured operational data.
  • Integrate agent-driven capabilities into Healthcare Digital’s platforms: Patient Meal Ordering: agentic nutrition checks, dietary rule enforcement, personalized recommendations. Food Production: prep-planning agents, demand forecasting agents, and waste-reduction optimization loops. EVS Task Management: task-ranking agents, routing agents, and real-time environmental monitoring copilots.
  • Build AI copilots for associates and managers that support decision-making, reduce administrative load, and automate repetitive tasks.
  • Ensure AI agents interact seamlessly with UI workflows, APIs, product logic, and underlying data systems.
  • Build and deploy predictive models that feed agent decision-making, including: Meal demand forecasting, EVS task prediction and prioritization, Labor and staffing optimization, Anomaly detection for operational issues
  • Integrate model outputs with MCP-based agents to create closed-loop automation—agents that both detect and act, not just analyze.
  • Translate findings into usable insights, dashboards, and operational recommendations for field teams.
  • Coach and mentor a team of data scientists, ML engineers, and AI engineers focused on agent development and MCP integration.
  • Partner with Healthcare Leadership (Culinary, EVS, Clinical Nutrition, Operations) to drive AI adoption and prioritize high-value opportunities.
  • Collaborate with Compass Digital, IT, and enterprise AI teams to align on architecture, security, and platform standards.
  • Communicate complex AI and agent-based system concepts to non-technical stakeholders in clear, practical language.
  • Ensure all AI and agent systems adhere to Compass Group’s governance frameworks, including privacy, compliance, and HIPAA.
  • Establish monitoring, auditability, and retraining workflows for both models and agents.
  • Implement agent safety controls, including sandboxed tool access, role-based permissions, and fallbacks for critical tasks.
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