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About This Role
About the Role
As Staff Software Engineer, you will be part of a dynamic, cross-functional team dedicated to enhancing the customer experience. You will collaborate closely with Design, Product Management, and Engineering experts to tackle real-world challenges and deliver innovative solutions that elevate our retail offerings.
What You’ll Do
- Lead the development of high-quality applications that are robust, observable and measurable. Using extreme programming (XP) practices and a user-centric approach
- Navigate the entire application lifecycle in collaboration with designers, product managers, and other engineers on the product team
- Leverage critical thinking, lead lean experimentation and influence decisions backed by data and industry best practices to implement desired business outcomes
- Establish and lead product engineering and software standards and continuously elevate existing standards using the new ones in the industry
- Mentor junior team members in pairing sessions, one-on-ones and ad-hoc as the need arises, and create teachable moments
- Contribute to cultural transformation by modeling partnership, collaboration, and technical initiatives and giving and receiving feedback regularly both within the team and across the organization
- Champion XP practice and knows when to customize it for effectiveness and also engages in conversations around practice, processes and community
- Additional tasks may be assigned
Addendum
STAFF AI SOFTWARE ENGINEER
- Design and build modular, headless AI backend services, advanced RAG architectures, and multi-agent orchestration workflows (e.g., LangChain, AutoGen, CrewAI).
- Construct enterprise-grade Model Context Protocol (MCP) gateways, Application Programming Interface (API) integration/mediation layers, rate limiters, and security proxies that allow autonomous agents to safely read and write to enterprise systems.
- Develop lightweight, streaming simulation frontends ("Dry-Run" interfaces) solely to validate agent behaviors, prompt strategies, and human-in-the-loop workflows during pilot discovery.
- Collaborate with Applied/Data Science and Machine Learning specialists to fine-tune Small Language Models (SLMs), execute weight quantization (AWQ/GPTQ), and optimize GPU memory utilization for high-throughput inference.
- Instrument distributed tracing, token utilization logging, and inference latency telemetry across the AI stack.
- Package reusable code libraries, containerized microservices, and integration SDKs for domain delivery and implementation teams.
- Architect resilient Compound AI Systems that orchestrate dynamic routing between deterministic APIs, specialized SLMs, and frontier LLMs.
Requirements
- 6+ years of demonstrated experience building and scaling production applications using foundation models, RAG, or agentic workflows
- 6+ years of experience integrating AI applications securely with enterprise data, APIs, and identity/access controls
- 4+ years of experience evaluating and optimizing AI application quality, latency, reliability, and cost
What Skills You Have
Required
- 6+ years of experience in software development
- Proven ability to manage multiple competing priorities
- Advanced knowledge of application design patterns, event-driven architecture, database schemas, and testing strategies
- Experience with large-scale application troubleshooting and performance tuning
- Demonstrated experience working with major cloud platforms (GCP, AWS, or Azure)
- Knowledge and experience with continuous integration, continuous deployment, and test-driven development
Preferred
- Bachelor's Degree or equivalent in MIS, Computer Science or related field
- Familiarity and experie
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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Kohl's, 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 $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Kohl's AI Hiring
Kohl's has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Menomonee Falls, WI, US, Menomonee Falls, WI.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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