Forward Deployed Engineer – Generative & Agentic AI

Cencora•Remote, KY

3d•Hybrid

About The Position

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. Apply today! About the Role We are seeking a Forward Deployed Engineer (FDE) to embed directly with business units and stakeholder teams, turning real-world problems into working, production grade Generative and Agentic AI solutions in days and weeks, not months. This is a hands-on, high ownership role that blends software engineering, solution architecture, and consulting: you scope the problem alongside the business, build the solution inside their actual systems and data, and feed what you learn back into our shared AI platform, so every future engagement moves faster. If you thrive in ambiguity, love shipping fast, and want your code to matter on day one, this role is built for you.

Requirements

Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
8+ years of overall software engineering / IT experience.
3+ years of hands-on experience building Generative AI / Agentic AI systems.
Strong expertise in Python and modern AI development frameworks.
Practical experience with LLMs (OpenAI, Claude, Gemini, Llama, or open weight models) and multiagent orchestration frameworks (LangGraph, AutoGen, or Semantic Kernel).
Deep understanding of RAG architectures, vector databases, and embedding models.
Experience with at least one major cloud platform (Azure, AWS, or GCP), including native AI services.
Proven experience with MLOps/LLMOps: CI/CD, containerization, observability, and evaluation frameworks.
Comfortable embedding directly with business or customer teams, including onsite engagement as needed.
Excellent communication skills — able to run stakeholder workshops and present to both executives and engineers in the same day.
Demonstrated ability to own a solution end-to-end in ambiguous, fastmoving environments, from scoping through production.

Nice To Haves

Prior forward deployed, consulting, or solutions architecture experience.
Familiarity with Model Context Protocol (MCP) and emerging AI agent ecosystems.
Experience building evals with tools like Ragas, Lang Smith, or custom harnesses.
Business or domain fluency in a regulated industry (healthcare, finance, or government) where data sensitivity and compliance shape the solution.
Track record of building reusable frameworks or components that scale across multiple engagements.
Healthcare industry knowledge alongside AI expertise.
Experience with healthcare distribution, specialty pharma data, or healthcare EMR/EHR systems (HL7, FHIR, claims, ND level data) is a significant advantage.

Responsibilities

Embed directly with business units and stakeholder teams to understand their workflows, data, and pain points firsthand, not from a spec document.
Scope and ship working Generative AI / Agentic AI solutions (POCs, MVPs, and production features) directly inside the business's real systems and data, in days to weeks cycles.
Build RAG pipelines, apply prompt engineering, and stand-up evaluation frameworks ("evals") that prove a solution is accurate enough to trust before going live.
Design and build API first integrations that connect AI capabilities into existing enterprise systems and workflows.
Translate ambiguous business needs into technical specs and translate technical tradeoffs back into language executives and business stakeholders can act on.
Lead stakeholder workshops, present progress transparently, and proactively flag risks before they become blockers.
Stay engaged through stabilization and adoption and fixing issues as they surface in the real world, not just at handoff.
Feed improvements, reusable components, and lessons learned back into the shared AI platform and toolkit so every future engagement benefits.
Apply MLOps/LLMOps practices (CI/CD, monitoring, model/agent lifecycle management) to keep deployed solutions reliable in production.