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About This Role
Who We Are
Robert Half is seeking a Software Engineer II – AI Engineer who will analyze, design, program, debug, test, implement, deploy, and support software enhancements and new applications using Generative AI technologies. This role contributes to the development and production deployment of GenAI\-enabled applications, including LLM\-powered workflows, RAG pipelines, and AI\-driven user experiences.
This role supports SDLC documentation across all phases, with a focus on deployment, evaluation, observability, safety, and monitoring. It also interacts with users to define requirements and support applications in production.
What You’ll Do
- Develop and modify application modules, including GenAI components.
- Build prompt workflows, retrieval layers, APIs, and cloud services.
- Troubleshoot production issues, including latency, hallucinations, and errors.
- Provide Level II production support for deployed systems.
- Design components, including LLM integrations and RAG pipelines.
- Implement CI/CD pipelines, containerization, and release processes.
- Develop RAG pipelines with embeddings, chunking, and vector search.
- Apply prompt engineering techniques, including few\-shot prompting and structured outputs.
- Evaluate models for accuracy, relevance, and hallucination risk.
- Implement safety guardrails, including PII protection and prompt\-injection defense.
- Execute testing, including unit, integration, and GenAI evaluation testing.
- Monitor production systems for latency, cost, usage, and errors.
- Support incident management with fallback and recovery strategies.
What You’ll Need
- 4\+ years of experience in IT or a related field.
- 2\+ years of software engineering experience.
- 1\+ year of experience in GenAI deployment.
- Experience with AI coding agent–augmented development.
- Experience with cost optimization, including token and caching strategies.
- Experience with Python, Java, C\#, JavaScript, or SQL.
- Experience building and deploying applications.
- Knowledge of cloud platforms, containers, and CI/CD.
- Understanding of SDLC, APIs, and system architecture.
- Knowledge of databases and data integration.
- Understanding of LLM fundamentals and token behavior.
- Experience with LLMOps/MLOps, including versioning and experiment tracking.
- Experience with prompt engineering techniques.
- Experience with RAG pipelines, including embeddings and vector search.
- Familiarity with evaluation metrics, including accuracy and hallucination risk.
- Knowledge of GenAI debugging and safety mechanisms.
- Knowledge of deployment governance, including access control and compliance.
- Experience with observability, including logging, tracing, and monitoring.
- Experience with Azure, AWS, or GCP.
- Strong communication and requirements\-gathering skills.
Preferred Generative AI Skills
- Experience with model orchestration, including multi\-step workflows and agents.
- Experience with LangChain, Semantic Kernel, or AutoGen.
- Experience designing embeddings and semantic search solutions.
- Experience with experimentation and A/B testing of prompts and models.
- Experience with conversational UX and human\-AI interaction design.
- Experience with incident handling, including retries and graceful degradation.
The typical annual salary range for this position is shown below and is negotiable depending upon experience and location. The position is eligible for a discretionary annual bonus.
$85,000\.00 \- $124,000\.00
We offer exceptional earning potential and a competitive benefits package, including group health insurance benefits (medical, vision, dental), FSA and HSA healthcare accounts, life and accident insurance, adoption and fertility assistance, paid parental leave of up to 6 weeks, and short/long term disability. Robert Half provides paid time off for vacation, personal needs, and sick time. The amount of Choice Time Off (CTO) our people receive varies based on their years of service and is pro\-rated based on the hours worked per week. A new hire earns up to 17 days of CTO per calendar year. Our people also receive up to 11 paid holidays per calendar year. We also offer the opportunity to contribute to our company 401(k) savings and investment plan or deferred compensation plan (if eligible), with an employer match of 100% on the first 3% of your contributions for eligible employees. Learn more at https://roberthalfbenefits.com.
Robert Half Inc. is an Equal Opportunity Employer. M/F/Disability/Veteran
As part of Robert Half’s Corporate Services facility employment process, any offer of employment is contingent upon successful completion of a background check.
Our recruiters use their expertise and may utilize AI to help with their evaluation of candidates.
Robert Half is committed to being an equal employment employer offering opportunities to all job seekers, including individuals with disabilities. If you believe you need a reasonable accommodation in order to search for a job opening or to apply for a position, please contact us by sending an email to HRSolutions@roberthalf.com or call 1\.855\.744\.6947 for assistance.
In your email please include the following:
- The specific accommodation requested to complete the employment application.
- The location(s) (city, state) to which you would like to apply.
For positions located in San Francisco, CA: Robert Half will consider qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
For positions located in Los Angeles County, CA: Robert Half will consider for employment qualified applicants with arrest or conviction records in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Salary Context
This $85K-$124K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $183K across 194 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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Robert Half, 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($104K) sits 52% below the category median. Disclosed range: $85K to $124K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Robert Half AI Hiring
Robert Half has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Ramon, CA, US. Compensation range: $124K - $124K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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