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Principal AI Engineer; Software & AI Labs

Job in Colorado Springs, El Paso County, Colorado, 80509, USA
Listing for: Keysight Technologies
Full Time position
Listed on 2026-06-26
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 138960 - 231600 USD Yearly USD 138960.00 231600.00 YEAR
Job Description & How to Apply Below
Position: Principal AI Engineer (Software & AI Labs)

Overview

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Candidate Responsibilities
  • Guiding safe adoption of AI Tools and applications.
  • Benchmarking commercial models and industry patterns for software engineering and guiding SAL to make the right decisions for Keysight’s engineering teams.
  • Developing Agentic/RAG solutions for developer workflows and Dev Sec Ops  Processes with an objective to radically evolve Keysight’s SDLC.
  • Using data‑driven indicators from Software Engineering Intelligence (SEI) platform to help SAL codify best practices and quantify AI’s impacts on Keysight’s development velocity.
  • Driving enablement via enterprise forums/guilds and playbooks to scale AI across teams.
  • Ensuring policy‑aligned, secure, and compliant AI usage across SDLC (e.g., ISO/IEC 42001, NIST AI RMF, SOC2, OWASP API Security).
Responsibilities

To be successful, the role requires an engineering mindset to manage, design, and execute on the following 4 pillars:

AI‑led Productivity & SDLC Acceleration
  • Lead technical evaluations and rollouts of AI tools for the enterprise and work with partners to define adoption of roadmaps and guardrails.
  • Design high‑impact solution patterns (prompt libraries, RAG architectures, agent workflows) for planning, coding, testing, documentation – with a goal to improve productivity.
  • Build reference implementations and “golden paths” that integrate AI with SSF tool chains.
  • Evaluate upcoming AI technology concepts (MCP, Agentic AI) and guide Software Engineering teams to safe adoption.
Architecture & Integration
  • Architect end‑to‑end AI solutions that bridge or span on‑prem, cloud, or VPC resources; optimize solutions for faster cadence, lower cost, and greater developer experience.
  • Partner with Central Engineering team to embed AI into planning, testing, documentation, CI/CD, and release processes.
Governance, Risk & Compliance
  • Translate corporate AI governance into developer‑friendly approaches: data handling, prompt safety, model access tiers, and vendor usage rules.
  • Align practices broadly to concepts as defined in ISO/IEC 42001 (AI management systems), NIST AI RMF, SOC 2 controls, and OWASP API Security within SAL’s Secure Software Factory (SSF) context.
Stakeholder, Vendor & Program Management
  • Evaluate and manage vendor relationships, set up pilot partnering with businesses and IT, benchmarking, and compliance obligations.
  • Engage with engineering leaders, IT, and adjacent business units to coordinate rollouts and validation sessions.
  • Provide concise executive updates and decision briefs on adoption, impact, and risks.
Qualifications

Education

  • Bachelor’s or master’s in computer science, electrical/electronic engineering, or related field; advanced ML/AI coursework or certifications preferred.

Must Have

  • Experience:

    8–12 years in Software engineering with either academic degree or interest in AI/ML, with experience in applying modern AI capabilities (LLMs, RAG, agents) to developer workflows at scale.

OR

  • 5–8 years in software engineering with demonstrated experience in applying modern AI capabilities (LLMs, RAG, agents) to developer workflows at scale.
  • Technical depth, functional experience in one or more of the following:
  • LLMs & orchestration (prompting, tooling, evaluation, safety); agent frameworks; retrieval pipelines.
  • Toolchain fluency:
    Dev Sec Ops  tools.
  • Neural Networks, Machine learning, MLOps.
  • Data engineering for AI use cases (extract‑transform‑Load (ETL); embeddings, vector stores), and ML Ops practices.
  • Security & compliance:
    Working knowledge of Security; experience in implementing practical security controls for developer teams.
  • Communication & leadership:
    Strong ability to drive change, run enablement programs, and influence stakeholders across global teams.

This position requires the ability to obtain and maintain a U.S. government security clearance…

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