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About This Role
Job Description
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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.Key responsibilities
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- 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.
Qualifications
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Required qualifications
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- 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.
Preferred qualifications
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- 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.
Additional Details
This job has a full time weekly schedule. Applications for this job will be accepted until at least June 24, 2026 or until the job is no longer posted.
The full\-time equivalent pay range for this position is $172,512\.00 \- $269,550\.00/yr plus eligibility for bonus, stock and benefits. Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations
Agilent Technologies, Inc. is an Equal Employment Opportunity and merit\-based employer that values individuals of all backgrounds at all levels. All individuals, regardless of personal characteristics, are encouraged to apply. All qualified applicants will receive consideration for employment without regard to sex, pregnancy, race, religion or religious creed, color, gender, gender identity, gender expression, national origin, ancestry, physical or mental disability, medical condition, genetic information, marital status, registered domestic partner status, age, sexual orientation, military or veteran status, protected veteran status, or any other basis protected by federal, state, local law, ordinance, or regulation and will not be discriminated against on these bases. Agilent Technologies, Inc., is committed to creating and maintaining an inclusive in the workplace where everyone is welcome, and strives to support candidates with disabilities. If you have a disability and need assistance with any part of the application or interview process or have questions about workplace accessibility, please email job\[email protected] or contact \+1\-262\-754\-5030\. For more information about equal employment opportunity protections, please visit www.agilent.com/en/accessibility.Travel Required:
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10% of the TimeShift:
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R\&D
Salary Context
This $172K-$269K range is above the median for AI Software Engineer roles in our dataset (median: $190K across 251 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,133 AI roles we're tracking, AI Software Engineer positions make up 8% of the market. At Agilent Technologies, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 863 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($221K) sits 5% below the category median. Disclosed range: $172K to $269K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Agilent Technologies AI Hiring
Agilent Technologies has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $269K - $328K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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