IT Engineer,Software,Expert

Overview Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 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. Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers. Responsibilities Platform Optimization & Org Health Hunt down technical debt — dead automations, bloated triggers, redundant fields, and queries that scan half the database for one record. Audit and rationalize automations into a single, sane framework. No more “which of the five automations on this object actually fires first?” mysteries. Monitor org health proactively — governor limits, storage, API consumption, deployment stability — using Salesforce Optimizer, Event Monitoring, and custom dashboards you build yourself. Development & Code Quality Build production-grade Apex — bulkified, governor-limit-aware, single-trigger-per-object, with proper separation of concerns (service/selector/domain layers). Develop Lightning Web Components that are reusable, accessible Hold the quality bar in code reviews — meaningful test assertions (not just coverage farming), no hardcoded IDs, clean error handling, sensible naming. Own and improve the CI/CD pipeline — scratch orgs, source-driven development, automated test gates, and deployments that don’t need a war room. Integration & Data Management Design and maintain integrations via REST/SOAP APIs, Platform Events, and Change Data Capture — with proper retry logic, error handling, and idempotency. Champion data quality — deduplication strategies, validation rules that actually help, and migration practices that don’t create a cleanup project of their own. Troubleshoot sync failures and API limit issues across connected systems before they become someone’s escalation. Collaboration & Best Practices Enablement Define and document development standards and best practices — then make sure the team actually follows them. Mentor junior developers through pairing, reviews, and knowledge sharing — not lectures. Evaluate every Salesforce release (three per year) for features worth adopting and deprecations worth addressing. Help the team make smart clicks-vs-code decisions so the org stays maintainable as it grows. Qualifications Qualifications Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience. 9–15 years of hands-on Salesforce platform development — cloud-agnostic, problem-first. Active Salesforce certifications preferred — Platform Developer I/II, Application Architect, System Architect, or any specialist certification. Clear communicator who can explain a complex technical tradeoff to a business stakeholder without their eyes glazing over. Core Technical Skills — Must Have Deep hands-on skills in Apex, SOQL/SOSL, LWC, and enough Visualforce knowledge to maintain legacy components. Strong JavaScript/ES6+, HTML, and CSS fundamentals. Expertise in Salesforce’s security architecture — profiles, permission sets, sharing rules, FLS, OWDs, role hierarchy. Solid Git workflow habits — branching, pull requests, merge conflict resolution. Proficiency with Salesforce CLI, scratch orgs, metadata API, and at least one CI/CD tool (GitHub Actions, Copado, Gearset). Sharp debugging instincts — Debug Logs, Query Plan tool, Limit monitoring, and the patience to trace a bug through five layers of automation. Familiarity with data migration/ETL tools — Data Loader, Dataloader.io, or equivalent. Comfortable in Agile/Scrum rhythms without needing a ceremony for every decision. Good to have AI Exposure Understanding of AI/ML fundamentals — supervised vs unsupervised learning, model training, evaluation metrics, and data pipelines. Exposure to Large Language Models (LLMs) — prompt engineering, fine-tuning concepts, RAG (Retrieval-Augmented Generation), and API-based integrations. Familiarity with generative AI use cases in enterprise contexts — summarization, classification, content generation, conversational bots, and intelligent search. Experience using AI-assisted development tools for code generation, test scaffolding, or automated code review. Basic comfort with Python or similar languages for scripting AI/ML workflows, data wrangling, or prototyping. Awareness of responsible AI principles — bias, explainability, data privacy, and governance considerations. Careers Privacy Statement***Keysight is an Equal Opportunity Employer.*** Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***