Senior Agentic AI Architect/Engineer

Rockwell Automation•United States of America California (remote), MA

2d•$146,880 - $220,320•Remote

About The Position

As a Senior Agentic AI Architect/Engineer, you will be a technical leader within our AI Engineering organization. You will implement our enterprise AI vision by architecting Generative AI and Agentic AI solutions. You have experience leading projects, optimizing ML systems for performance, and building MLOps infrastructure in cloud-native environments. You will report to the Director of Agentic AI. You will work remote.

Requirements

Bachelor's Degree in Relevant Field or Equivalent Years of Relevant Experience.
Legal authorization to work in the U.S.
Expert-level proficiency in Python and SQL.
Hands-on experience developing and deploying Generative AI solutions, RAG pipelines, and multi-agent frameworks, regardless of the underlying cloud vendor.
5+ years of experience with major cloud platforms, specifically Microsoft Azure, AWS, or Google Cloud Platform (GCP).
1+ years of experience working with and integrating with ERP solutions like IFS Cloud
1+ years of experience working with and managing partners through end-to-end solution design through delivery
Worked on core MLOps and orchestration tools, including Docker, Airflow
Worked with vector search platforms like Azure Cosmos DB for PostgreSQL, Redis, Azure AI Search, or pgvector/Mongo Atlas.
Familiarity with classical ML modeling techniques such as Causal Inference, Matrix Factorization, XGBoost
Familiarity with Scaled Agile Framework (SAFe) methodologies.

Nice To Haves

Typically requires 8+ years of relevant work experience.

Responsibilities

Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers.
Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines, including data ingestion, document processing, and promoting generation, such as using Azure OpenAI embeddings or Gemini embeddings.
Integrate with vector and NoSQL databases.
Increase model serving layers and high-throughput data processing pipelines, employing advanced libraries (e.g., Polars) to ensure ultra-low latency and efficient resource use across the cloud environment.
Design and implement secure system architectures for all new AI products, ensuring strict compliance with enterprise-wide data governance and security policies through mechanisms such as PII filtering and guarded conversation frameworks.
Use Terraform for provisioning and manage Azure infrastructure-as-code.
Implement centralized observability (logging and alerting) to support automated and reproducible Continuous Integration/Continuous Deployment (CI/CD) pipelines.