Senior Software Engineer — Full Stack & AI Platforms

Agilent Technologies•Santa Clara, CA

3d•$172,512 - $269,550•Onsite

About The Position

We’re looking for a senior full-stack engineer to build modern, user-facing applications and backend services while embedding AI-driven capabilities—such as predictive maintenance and smart diagnostics—into a regulated product environment. You’ll partner closely with the technical lead and play a key role in shaping both the architecture and developer experience.

Requirements

Bachelor's or Master's Degree or equivalent.
8+ years of full-stack software engineering (modern web frontend plus backend services).
Hands-on AI/ML experience — model integration, inference, and ideally predictive or diagnostic use cases.
Cloud-native development: Kubernetes, REST / gRPC, event-driven messaging; Go and/or .NET / C#.
Strong product sense and the ability to work spec-first in a regulated product domain.

Nice To Haves

Telemetry / IoT, fleet-management, or scientific software background.
Vector / RAG, MLOps, or edge-inference familiarity.
UI engineering with a modern component framework.
4+ years applying AI/ML in production, including building and deploying agentic AI in development and test tooling (AI-assisted coding, automated / AI-driven testing).
Deep, hands-on container orchestration (Kubernetes): containerizing services, deployment, and model serving / tuning.
Demonstrable hands-on experience in orchestrating modern Developer experience by engaging with DevOps and Platform teams
Experience delivering in rapid release cycles within a regulated environment (21 CFR Part 11 / GxP).
Experience converting legacy monolithic code into microservices — specifically workstation-based enterprise application to containerized client-server web application transformation.

Responsibilities

Build user-facing management UI and the supporting backend services (Go and/or .NET).
Design and prototype AI/ML-assisted features — predictive maintenance, smart diagnostics, and AI-assisted workflows.
Contribute to product specifications and API contracts; integrate with platform identity and data services.
Establish reusable AI patterns that can be generalized across the platform.
Apply agentic AI in the development and test workflow (AI-assisted coding, automated testing), and help migrate components from monolith toward microservices — moving workstation-based enterprise applications to a containerized client-server web model.