Technical Product Manager - AI Solutions (Customer Operations)

myhrabc; Conshohocken (Montgomery County); On-site; USD 140,000 - 190,000; Full time

Job summary

Cencora is seeking a Technical Product Manager specializing in AI Solutions to own the generative AI and automation product portfolio for Customer Operations. You will lead a dedicated engineering team, shaping AI-powered self-service and agent-assisted workflows.

This hands-on role requires shipping LLM-enabled automation in customer service, collaborating with service and platform teams, defining prompts and evaluation frameworks, and ensuring responsible AI delivery within a regulated

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Business, or a related field.

Responsibilities

• Own the product vision, roadmap, and backlog for AI in Customer Operations.
• Lead product discovery with customer service and platform stakeholders to identify high-value AI use cases.
• Develop PRDs, user stories, and functional specs for AI-driven capabilities.
• Create, test, iterate, and publish production-ready prompts and agent configurations.
• Define evaluation frameworks, launch quality bars, and monitor production behavior.

Skills

AI product management

LLM-enabled automation

Stakeholder management

Agile development

PRDs & user stories

Education

Bachelor's degree in Computer Science / Info Systems / Business

Tools

LangChain/LangGraph

Semantic Kernel

Microsoft Copilot Studio

Azure OpenAI

Job description

Our team members are at the heart of everything we do. At Cencora, we are united in our responsibility to create healthier futures, and every person here is essential to us being able to deliver on that purpose. If you want to make a difference at the center of health, come join our innovative company and help us improve the lives of people and animals everywhere.

Job Details

Role Summary

As a Technical Product Manager specializing in AI Solutions, this role will own the generative AI and automation product portfolio for Customer Operations at Cencora - both the systems our customers use directly and the systems our employees rely on when supporting them. You will serve as the product owner for a dedicated engineering team, driving a roadmap that spans AI-powered self-service, AI assistance for customer-facing support personnel, and increasingly automated resolution workflows.

This is a hands-on product role. You will lead discovery with service and platform teams, define assistant and agent behaviors, manage prompt and context engineering as a production discipline, build the evaluation frameworks that determine whether AI features are ready to ship, and iterate on live capabilities based on real-world performance. We're looking for someone who has built and shipped with this technology - not someone who has managed it from a distance.

The successful candidate combines customer operations domain knowledge with practical experience shipping LLM-enabled products. Direct experience implementing LLM-enabled automation workflows, agentic AI, or custom generative AI solutions - ideally in a customer service context - is required.

This role resides within our Global Data and Technology Services organization (GDATS) as part of the Data, Analytics and AI domain at Cencora.

Primary Duties and Responsibilities

• Own the product vision, roadmap, and backlog for AI in Customer Operations, acting as the product owner for an engineering team within our scaled agile planning cadence.
• Lead product discovery with customer service and digital platform stakeholders to identify, prioritize, and validate high-value AI use cases, with defined success metrics (e.g., containment/deflection, FCR, AHT, CSAT, adoption, response quality).
• Develop detailed product requirements documents (PRDs), user stories, and functional specifications for AI-driven capabilities - including assistant and agent behaviors, conversation and escalation flows, knowledge grounding and retrieval strategy, and guardrails.
• Create, test, iterate, and publish production-ready prompts and agent configurations for customer operations use cases, treating prompt and context engineering as an ongoing discipline rather than a one-time setup.
• Own AI quality end to end: define evaluation frameworks (test datasets, automated evaluation, human review), set launch quality bars, monitor production behavior, and drive continuous improvement.
• Advise and consult stakeholders on build vs. buy vs. configure decisions across our enterprise AI platform landscape, using a strategic, data-driven approach.
• Manage product development cycles with internal engineering and external vendor resources; oversee test planning, system integration testing, validation of results, and issue resolution.
• Partner with legal, compliance, privacy, and security stakeholders to deliver AI capabilities responsibly in a regulated healthcare environment.
• Work closely with end users to drive adoption, including training, enablement, feedback loops, and support for AI tool usage.

Experience and Educational Requirements

• Bachelor's degree in Computer Science, Information Systems, Business, or a related field.
• 6+ years of relevant product management or related work experience.
• Demonstrated experience defining, deploying, and iterating on AI enabled solutions and automations in an enterprise or B2B/B2C product environment.

Required AI Experience

• Candidates should be able to speak concretely about AI products they have built and shipped.
• Experience implementing LLM-enabled automation workflows, agentic AI, or custom generative AI solutions in production - ideally for customer service or customer experience use cases. Examples include AI assistants or copilots for service agents, customer-facing conversational AI, intelligent triage and routing, and automated case resolution.
• Working familiarity with the modern generative AI stack: LLM APIs (Azure OpenAI, Anthropic, or comparable), retrieval-augmented generation and knowledge grounding, function/tool calling, and agent orchestration frameworks or platforms (e.g., LangChain/LangGraph, Semantic Kernel, Microsoft Copilot Studio, Salesforce Agentforce, or similar).
• Hands-on prompt engineering experience, including creating, testing, versioning, and optimizing prompts and conversational flows for customer-facing applications.
• Experience evaluating and monitoring AI quality - evaluation datasets, automated or LLM-assisted evals,