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About This Role
Who We Are:
At Avnet, relationships matter. We are a global, FORTUNE ® 500 technology distributor and solutions company that delivers design, supply chain and logistics expertise to customers at every stage of a product’s lifecycle. Our employees have a front row seat to the latest innovations shaping the world we live in and the future we share. We’re driven to help our customers around the world succeed and we do so by earning the trust of some of the biggest names in technology.
Working at Avnet means being a part of a global team. We work collaboratively and with integrity, doing business the right way. For more than a century, we have partnered together to help our customers, suppliers and teammates realize the transformative possibilities of technology. Experience what’s next at Avnet!
This role is fully onsite.
Job Summary: Develops, maintains, and enhances business applications with a specialized focus on building, validating, and optimizing intelligent cloud services. Collaborates with stakeholders to validate user requirements, assess available technologies, and recommend technical strategies to deliver production\-grade Generative AI features. Assesses objectives for assigned project phases and recommends technical tactics to achieve business needs through modern full\-stack development, Agentic AI, and AI solutions.
Principal Responsibilities:
- Develop and implement AI models and algorithms using Microsoft Azure technologies to optimize business processes and improve efficiency.
- Uses process design technology methodologies, programming languages and tools, and solutions design techniques to develop full\-stack applications to meet business specifications.
- Performs analysis, design, development, and testing of applications to solve business requirements, actively leveraging Azure AI to provision resources, manage foundation model endpoints, and orchestrate LLM workflows.
- Builds and tunes production\-grade RAG pipelines (including document ingestion, semantic chunking, embedding generation, vector indexing, and hybrid retrieval optimization).
- Collaborates on the development of semi\-autonomous workflows or Agentic AI systems, focusing on tool integration, robust error handling for non\-deterministic LLM outputs, and latency management.
- Integrates advanced Azure AI services and LLM workflows with existing enterprise Angular front\-ends and Java/Spring Boot back\-ends, ensuring secure data transit, state management, and seamless UI/UX for AI\-driven features.
- Supports change readiness initiatives as needed.
- Other duties as assigned.
Job Level Specifications:
- Extensive knowledge and application of full\-stack engineering principles, theories, and concepts. Complete knowledge of all job functions and broad industry best practices, techniques, and standards regarding cloud\-native development and enterprise AI deployment.
- Develops solutions to complex problems where analysis of situations and/or data requires in\-depth evaluation of variables (such as query rewriting, vector database tuning, and prompt flows). Determines the best approach to achieve results and provides suggestions to improve policies, procedures, and system performance.
- Work is performed independently and requires the exercise of judgment and discretion. Exercises considerable latitude in determining objectives and approaches to assignments. Work may be reviewed at a high\-level.
- May represent the organization as a primary contact on assignments and/or projects. Interacts with senior professionals and management and frequently coordinates work between departments or organizations.
- Actions may impact the organization. Failure to accomplish work will result in the inability to reach crucial organizational goals. Erroneous decisions may have a prolonged effect resulting in the expenditure of substantial resources.
Work Experience:
- Typically 8\+ years with bachelor's or equivalent..
Education and Certification(s):
- Bachelor's degree or equivalent experience from which comparable knowledge and job skills can be obtained.
Distinguishing Characteristics:
- Azure AI Ecosystem: Hands\-on experience navigating Azure AI Foundry / Azure AI Studio to configure hubs, deploy foundation models (e.g., GPT series, Llama), build prompt flows, and integrate vector search.
- Full\-Stack Project Delivery: Proven project experience in full\-stack application development utilizing Angular(or similar technologies) and Java.
- Core Languages \& Frameworks: Building and coding applications using languages/technologies such as Java, Python, Spring / Spring Boot, Web Services, and SQL.
- Frontend Technologies: Production\-level experience using Angular, TypeScript, HTML, CSS/Sass, and Bootstrap.
- RAG \& Agentic Patterns: Understanding of strategic document chunking, embedding selection, vector stores (e.g., Azure AI Search, pgvector), and exposure to multi\-turn agent frameworks or semantic orchestration patterns.
- Emerging AI Standards (Preferred): Familiarity with the Model Context Protocol (MCP) for building secure, standardized connections between LLM applications, enterprise data sources, and business tools/APIs.
- Database \& Data Management: Practical knowledge of SQL database design, optimization, and complex dataset retrieval. Familiarity with cloud data tools like Databricks is a plus.
- Testing \& Evaluation: Experience utilizing testing tools like JUnit for Java, coupled with an awareness of LLM evaluation practices (assessing grounding, relevance, and latency metrics).
- CI/CD \& DevOps: Experience participating in automated deployment and integration pipelines using GitHub Actions, Jenkins, Maven, or Gradle.
Methodologies: Strong alignment with SDLC practices including Agile/Scrum and structured configuration environments.
\#LI\-AMER
What We Offer:
Our employees work hard to live our values and help us grow. Our total rewards strategy supports Avnet’s ability to attract, engage, develop, and reward our employees, while promoting a diverse and inclusive environment. We offer competitive compensation and benefit programs — from time away and flexible working arrangements to programs supporting employee well\-being and opportunities to give back to your community.
- Generous Paid Time Off
- 401K and Pension Plan
- Paid Holidays
- Family Support (Paid Leave, Surrogacy, Adoption)
- Medical, Dental, Vision, and Life Insurance
- Long\-term and Short\-term Disability Insurance
- Health Savings Account / Flexible Spending Account
- Education Assistance
- Employee Development Resources
- Employee Wellness, Leadership Development and Mentorship Programs
Benefits listed above may vary depending on the nature of your employment with Avnet.
The above statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills.
Avnet is an Equal Opportunity Employer committed to providing equal opportunities to all employees and applicants for employment without regard to race, color, religion, ancestry, national origin, sex (including pregnancy), age, marital status, sexual orientation, gender identity or expression, disability, veteran status, genetic information or any other characteristic protected by law. This policy of non\-discrimination also applies to religious dress and grooming practices. Avnet will accommodate employee religious dress standards and grooming practices that do not result in undue hardship for the Company. If you are interested in applying for employment with Avnet and need special assistance or an accommodation to apply for a posted position contact our Human Resources Service Center at (888\) 994\-7669\.
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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Avnet, 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 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Avnet AI Hiring
Avnet has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Tempe, AZ, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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