Agilent is expanding its AI capabilities across scientific, service, commercial, and operational functions. We are building a team of engineers who can help design, deploy, and scale trusted AI solutions that create measurable business impact. 

We're interested in connecting with experienced professionals across AI Engineering, AI Data Engineering, and related technical disciplines who are passionate about applying AI in real-world environments. Depending on your background and interests, opportunities may focus on AI applications, agentic systems, retrieval foundations, AI-ready data products, platform engineering, or the underlying capabilities that enable AI at scale. 

You will work within cross-functional AI pods alongside engineers, domain experts, business stakeholders, and platform teams to turn complex business challenges into practical, production-ready solutions. 

What You May Work On 

• Building production AI applications, agents, and AI-enabled workflows. 

• Designing retrieval foundations for AI solutions, including RAG, vector search, semantic enrichment, and knowledge assets. 

• Developing AI-ready data products with strong foundations in quality, governance, lineage, and reuse. 

• Creating reusable capabilities, patterns, and assets that accelerate future AI use cases across the enterprise. 

• Applying evaluation, monitoring, observability, and governance practices to ensure reliable AI outcomes. 

• Partnering directly with domain experts to understand workflows and translate business needs into scalable technical solutions. 

• Leveraging AI-assisted approaches such as metadata generation, entity resolution, and content classification to improve data quality and discoverability. 

We're Particularly Interested in Professionals with Experience In:

AI Engineering 

• AI agents and Agentic Workflows 

• Retrieval-Augmented Generation (RAG) 

• LLM application development 

• AI evaluation and observability 

• AI orchestration and integration 

• Production AI deployment 

AI Data Engineering 

• Data products and data contracts 

• AI-ready data foundations 

• Metadata management and semantic enrichment 

• Retrieval architecture 

• Vector databases and graph technologies 

• Data quality, governance, and lineage 

Software & Platform Engineering 

• Full-stack software development 

• Distributed systems and APIs 

• Cloud platforms and enterprise integration 

• Platform engineering 

• Identity, access, and governance controls