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Key skills for this role
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.
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.
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.
Skip the repetitive application forms
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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.
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.***
Global electronic design, test, and manufacturing hardware/software solutions.
Visit company websiteJobs and hiring trendsFull-time
Senior · 5+ years experience
Onsite
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