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Lead, AI Engineering & SDLC Automation

Agilent
Santa Clara, USA
Full-time
Senior
Hybrid
USD 168160-315300 yearly / year
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Job Description

We’re looking for a Lead, AI Engineering & SDLC Automation to join our Developer Experience team and provide technical leadership for Productivity Solutions Division’s (PSD) AI engineering and SDLC automation efforts. This Expert individual contributor role will focus on using AI and automation to improve how our teams build, test, secure, and deliver software.

As part of Platform Engineering, you’ll help improve the development experience across PSD by bringing new capabilities and automation into the tools, platforms, and workflows our development community uses every day. This is a hands-on role that will help shape technical direction, evaluate new technologies , build solutions, and help teams adopt new tools and ways of working.

Impact

Your work will help PSD reduce manual effort, improve developer experience, and make software delivery more consistent and efficient. Success means turning AI capabilities into practical solutions that teams use and that measurably improve how software is developed and delivered.

What You’ll Do

Provide technical leadership for AI engineering and SDLC automation across PSD.

Shape the technical strategy, roadmap, and delivery approach for AI and automation in software engineering.

Identify, evaluate, and recommend tools, agents, copilots, platforms, and automation opportunities.

Design and deliver engineering tools, agentic workflows, copilots, and automation that improve developer productivity and software delivery.

Integrate these capabilities into continuous integration and delivery pipelines, developer paved roads, engineering workflows, and platform services.

Establish practical standards, governance practices, evaluation methods, safety guardrails, and secure integration patterns for responsible use of AI.

Partner with DevSecOps , Security, Quality Engineering, IT, platform teams, and engineering teams to bring automation into software delivery practices.

Lead complex, cross-functional initiatives from discovery and experimentation through implementation, adoption, and measurement.

Build reference implementations and working examples that make it easier for teams to adopt new capabilities.

Provide technical guidance and mentoring to engineers and teams adopting new development practices.

Define and track measures of success, including adoption, developer productivity, toil reduction, quality, reliability, and developer experience.

Represent PSD in technical forums and contribute technical expertise to broader AI engineering and automation efforts across Agilent.

What You Bring

Typically 8+ years of relevant experience in software engineering, platform engineering, DevOps, developer productivity, workflow automation, SDLC automation, or related areas.

Bachelor’s or Master’s degree in computer science, engineering, or a related field, or equivalent experience.

Strong software engineering experience, including building integrations, automation, APIs, developer tooling, or platform capabilities.

Hands-on experience building or integrating AI agents, copilots, LLM-based solutions, agentic workflows, or similar capabilities.

Experience with continuous integration and delivery, SDLC tooling, developer workflows, and cloud or platform services.

Ability to take broad or ambiguous engineering problems and turn them into practical technical solutions.

Understanding of responsible AI practices, including governance, safety guardrails, evaluation methods, usage controls, and secure integration.

Experience leading technical work across teams through expertise , collaboration, and influence.

Experience helping engineering teams adopt new tools, technologies, and development practices.

Ability to balance developer productivity with quality, reliability, security, cost, and responsible AI use.

Ability to communicate complex technical topics clearly with engineers, technical leaders, and senior stakeholders.

Experience solving difficult engineering problems, sharing knowledge, and making an impact beyond a single team or project.

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