Sr. Manager, Product Development (AI & Automation Product Management)

Cigna Healthcare•Tennessee Work at Home, TN

1d•$130,900 - $218,100•Remote

About The Position

The Sr. Manager of Product Development (AI & Automation Product Management) leads the strategy, design, and delivery of both AI-driven and rules-based automation products. This role is responsible for advancing intelligent automation across the enterprise by combining advanced AI/ML capabilities with traditional workflow, rules, and process automation solutions. The leader will drive a cohesive product strategy that integrates AI-enabled decisioning with deterministic automation to deliver scalable, efficient, and compliant operational outcomes. This role requires strong product leadership, technical fluency across AI and automation paradigms, and the ability to guide cross-functional teams from ideation through deployment and continuous optimization.

Requirements

Bachelor’s degree in Business, Engineering, Computer Science, or a related field, or equivalent work experience required; MBA preferred.
8+ years of product management experience, including: AI/ML-based products
8+ years of product management experience, including: Workflow, RPA, or rules-based automation solutions
Proven success delivering complex automation products at scale
Strong understanding of: AI/ML concepts (NLP, LLMs, predictive modeling)
Strong understanding of: Business process automation (workflow engines, decision rules, RPA)
Experience working in agile, cross-functional environments
Strong leadership, communication, and stakeholder management skills
If you will be working at home occasionally or permanently, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload.

Nice To Haves

Experience in healthcare or other regulated industries
Familiarity with document intelligence, clinical decision support, and case management systems
Experience with cloud platforms (Azure, AWS, GCP) and automation tools
Knowledge of MLOps and automation lifecycle management frameworks

Responsibilities

Define and execute a unified product strategy that spans AI/ML solutions and non-AI automation (e.g., rules engines, workflow orchestration, RPA)
Identify and prioritize opportunities to optimize processes using the right approach (AI vs. deterministic automation vs. hybrid)
Develop integrated product roadmaps aligning business goals, operational efficiency, and customer experience
Drive build vs. buy vs. partner decisions across AI platforms and automation tooling
Lead end-to-end lifecycle for AI and non-AI automation products: ideation, requirements, design, build, launch, and continuous improvement
Translate business and operational needs into product requirements, user stories, and automation logic
Deliver: AI-driven solutions (e.g., NLP, document intelligence, predictive models)
Deliver: Rules-based automation (e.g., decision engines, workflows, straight-through processing)
Ensure scalability, resiliency, and maintainability across automation solutions
Partner with data scientists, engineers, and automation teams to implement: Machine learning models and LLM-based capabilities
Partner with data scientists, engineers, and automation teams to implement: Workflow automation engines and orchestration platforms
Partner with data scientists, engineers, and automation teams to implement: Document processing and structured/unstructured data pipelines
Design hybrid solutions combining: AI inference + business rules
Design hybrid solutions combining: Human-in-the-loop workflows where needed
Ensure proper model lifecycle management (MLOps) alongside automation lifecycle governance
Deeply understand and optimize: End-to-end workflows across intake, review, decisioning, and document processing
Deeply understand and optimize: Integration points between AI solutions and deterministic automation systems
Identify opportunities to: Increase straight-through processing rates
Identify opportunities to: Reduce manual intervention
Identify opportunities to: Improve accuracy, cycle time, and operational scalability
Analyze user workflows and operational bottlenecks to determine optimal automation approaches
Balance accuracy, explainability, and efficiency in AI vs. rule-based decisioning
Ensure seamless user experiences across automated and human-driven processes
Drive adoption through intuitive design, transparency, and trust in automation outputs
Ensure all AI and automation solutions meet regulatory, compliance, and audit requirements
Establish governance frameworks for: AI model risk (bias, drift, explainability)
Establish governance frameworks for: Rules engine integrity and change control
Establish governance frameworks for: Automation failure handling and exception management
Partner with risk, compliance, and legal to maintain responsible and ethical solutions
Lead and mentor product managers across AI and automation domains
Collaborate with business, clinical, operations, IT, and data teams
Influence executive stakeholders with clear product vision, roadmap, and ROI-driven outcomes
Manage vendor relationships across AI platforms and automation tools
Define KPIs across both AI and non-AI automation, including: Automation rate / straight-through processing
Define KPIs across both AI and non-AI automation, including: Model accuracy and precision
Define KPIs across both AI and non-AI automation, including: Cycle time reduction
Define KPIs across both AI and non-AI automation, including: Cost savings and productivity gains
Establish feedback loops for: AI model retraining
Establish feedback loops for: Rules optimization
Establish feedback loops for: Workflow refinement