About this role
Job Description
We are seeking a Senior AI Agent Engineer to build AI applications connecting LLMs with enterprise systems, laboratory software, and scientific instruments. This back-end-focused individual contributor will own architecture, deployment, and operation using Python and TypeScript/Node.js, RAG, and secure agent orchestration. This is an applied software engineering role, not a model research position.
Key Responsibilities
- Design and deliver AI agent applications, back-end services, and APIs using Python and TypeScript/Node.js.
- Build agent runtimes with prompt and context management, tool calling, persistent state, recovery and human approval.
- Develop and optimize RAG pipelines, including ingestion, embeddings, vector/hybrid search, reranking, and source-grounded responses.
- Integrate approved cloud or local model APIs with enterprise or laboratory systems; build reusable MCP clients and servers.
- Own production quality through testing, CI/CD, access controls, prompt-injection defenses, and monitoring of accuracy, latency, cost, and failures.
- Lead architecture and delivery decisions, mentor engineers, collaborate across product, science, UX, quality, and support teams, and support AI interfaces when needed.
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Qualifications
Required Qualifications
- Bachelor’s degree or above in computer science, software engineering, AI, automation, data science, or a related technical field.
- 5+ years of software engineering experience, with production development in both Python and TypeScript/Node.js and strong foundations in asynchronous programming, API design, testing and debugging.
- Hands-on delivery of LLM applications or agents using cloud or local model APIs and a relevant framework or SDK, such as LangGraph, LangChain, Semantic Kernel, or LlamaIndex.
- Practical end-to-end RAG experience and proficiency with at least one vector database or search platform, including retrieval evaluation, metadata filtering, and relevance troubleshooting.
- Experience with prompt/context engineering, structured outputs, tool orchestration, state management and failure recovery; sound judgment on when deterministic workflows are preferable.
- Strong API, database, and authentication integration skills; familiarity with MCP, Docker, Linux, CI/CD, observability, least-privilege access, and secure handling of model outputs.
- Ability to own architecture and production delivery, collaborate across functions, communicate effectively in English, and support AI interfaces when needed.
Preferred Experience
- Advanced agent-platform experience: long-running or multi-agent workflows, reusable MCP services, evaluation pipelines, and operational recovery.
- Local inference or hybrid cloud/local deployments, advanced retrieval optimization, and scalable services using technologies such as FastAPI, Redis, queues, Kubernetes, or OpenTelemetry.
- Scientific software, laboratory automation, instrument integration, LIMS/CDS, regulated workflows, or integration with .NET/C# applications.
- Angular, Electron, or React for agent workspaces and approval interfaces; enterprise identity and Responsible AI practices.
Additional Details
This job has a full time weekly schedule.
Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations
Agilent Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.
## Travel Required:
Occasional
## Shift:
Day
## Duration:
No End Date
## Job Function:
R&D