AI Architect/AI Engineer – Generative & Agentic AI

Cencora•USA > TX > Remote, TX

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 an experienced hands-on AI Architect to lead the architecture, design, and delivery of enterprise-scale Generative and Agentic AI solutions. This is a hands-on architectural leadership role spanning system design, rapid proof-of-concept (POC) and MVP delivery, API-first solution architecture, and technical governance across our AI platform. The ideal candidate combines deep technical depth in LLMs, multi-agent orchestration, and RAG architecture with the ability to translate business needs into scalable, production-grade AI systems and to mentor engineering teams along the way.

Requirements

Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
6+ years of overall IT experience spanning software engineering, cloud architecture, and/or AI/ML.
3+ years of hands-on architecture experience specifically in Generative AI / Agentic AI systems.
Strong expertise in Python and modern AI development frameworks.
Demonstrated experience architecting solutions with LLMs (OpenAI, Claude, Gemini, Llama, or open-weight models).
Deep understanding of RAG architectures, vector databases (Pinecone, FAISS, Databricks Vector Databases, pgvector), and embedding models.
Experience architecting on at least one major cloud platform (Azure, AWS, or GCP), including native AI services (Azure AI Foundry / Azure OpenAI, AWS Bedrock, Google Vertex AI).
Proven experience with MLOps/LLMOps: CI/CD, containerization (Docker/Kubernetes), observability, and evaluation frameworks.
Strong grounding in AI governance, security, compliance, and Responsible AI practices.
Excellent communication skills, with the ability to present architecture to both executives and engineers.

Nice To Haves

Experience designing state management and persistent memory for long-running autonomous agents.
Familiarity with Model Context Protocol (MCP) and emerging AI agent ecosystems.
Experience with AI observability / evaluation tooling (LangSmith, Ragas, Langfuse, or custom eval harnesses).
Prior consulting, client-facing, or forward-deployed architecture experience.
Relevant cloud certifications (Azure AI Engineer/Architect, AWS ML Specialty, Google Professional ML Engineer).
Experience with NL-to-SQL, knowledge graphs, or GraphRAG-style architecture.
Healthcare domain preference: Candidates who bring healthcare knowledge alongside their AI expertise.
Experience with healthcare distribution, specialty pharma data, or healthcare EMR/EHR systems (HL7, FHIR, claims, NDC-level data) is a significant advantage.

Responsibilities

Own end-to-end architecture for enterprise Generative AI and Agentic AI solutions, from concept through production.
Lead rapid POC and MVP development to validate AI use cases and de-risk technical approaches before full build-out.
Architect scalable AI platforms leveraging LLMs, RAG pipelines, vector databases, and multi-agent orchestration frameworks (LangGraph, AutoGen, Semantic Kernel).
Design API-first architectures (REST/GraphQL) that expose AI capabilities to downstream applications and enterprise systems.
Define technology selection, architecture standards, and best practices for prompt engineering, model evaluation, and AI governance / Responsible AI.
Architect and guide MLOps/LLMOps practices for deployment, monitoring, and model/agent lifecycle management.
Lead architecture reviews and present designs to executive sponsors and engineering teams; drive stakeholder alignment.
Mentor AI engineers, set coding and architecture standards, and raise the technical bar across the team.
Evaluate and select cloud-native AI services (Azure AI Foundry, Google Vertex AI), balancing scalability, cost, security, and performance.

Benefits

Medical, dental, and vision care
Comprehensive suite of benefits that focus on the physical, emotional, financial, and social aspects of wellness
Support for working families, which may include backup dependent care, adoption assistance, infertility coverage, family building support, behavioral health solutions, paid parental leave, and paid caregiver leave.
Variety of training programs
Professional development resources
Opportunities to participate in mentorship programs, employee resource groups, volunteer activities, and much more.

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