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About This Role
At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.
AI Software Engineer – Cloud AI Platforms
At NICE, we are not just building software—we are transforming how cloud operations are run using AI. We are building intelligent platforms that can understand system behavior, make decisions, and automate real\-world operational workflows at scale. If you’re excited about applying AI beyond chatbots into real production systems, this is an opportunity to work on meaningful, high\-impact problems.
What’s the role all about?
As an AI Software Engineer, you will be part of a team building AI\-powered operational platforms that integrate across monitoring systems, CI/CD pipelines, ticketing tools, and cloud infrastructure. You will work on designing and implementing intelligent workflows, integrating AI models, and building scalable systems that automate complex operational tasks.
This is a highly hands\-on role focused on building, integrating, and scaling AI\-driven solutions in production environments.
How will you make an impact?
- Build and scale AI\-driven workflows and automation systems
- Develop integrations with systems like monitoring platforms, ticketing tools (ServiceNow, Jira, OpsGenie), CI/CD pipelines, and cloud services
- Design and implement APIs, tools, and data pipelines that power AI\-driven decision\-making
- Work on LLM integrations, prompt engineering, and orchestration layers — streaming responses, function calling, tool use, RAG pipelines, agentic orchestration
- Build and maintain full\-stack AI applications using TypeScript, React, and Next.js — from user dashboards and personalized experiences to real\-time analytics and interactive tools
- Translate real\-world operational problems into automated, intelligent solutions
- Collaborate with Product, SRE, and Infrastructure teams to deliver end\-to\-end capabilities
- Improve system performance, reliability, and observability
- Build evaluation and observability systems — measure model capabilities, monitor output quality, and create dashboards that keep the product improvable
- Create reusable platforms and tools that accelerate development — component libraries, shared abstractions, internal tooling that multiplies team productivity
Key Responsibilities
- Design and develop scalable backend systems for AI\-powered platforms
- Build and maintain AI integrations, workflows, and automation pipelines
- Implement REST APIs, microservices, and event\-driven architectures
- Design and implement database schemas and queries for complex domains — tracking, engagement, reporting
- Work with both structured and unstructured data for AI use cases
- Contribute to CI/CD pipelines, testing, and deployment automation
- Troubleshoot and optimize production systems
- Collaborate with cross\-functional teams to deliver high\-quality solutions
- Contribute to reusable frameworks and engineering best practices
- Prototype fast — move from concept to working demo in days, ship incrementally
What we’re looking for
- 5\+ years of software engineering experience, strong focus on full\-stack web development
- Expert in TypeScript and React — performance optimization, modern patterns (hooks, context, suspense), component architecture
- Production experience with Next.js — App Router, Server Components, API routes, SSR/SSG, edge deployment
- Hands\-on experience with LLMs — prompt engineering, streaming APIs, function calling, tool\-use, chaining and orchestration patterns
- Experience with Vercel AI SDK — unified LLM provider interface, streaming, structured output, tool calling across OpenAI/Anthropic/Google/xAI
- Model Context Protocol (MCP) — building or consuming MCP servers for extensible AI tool use
- Strong backend fundamentals — Node.js or Python, REST/GraphQL APIs, relational databases, Redis, auth
- Solid database design — PostgreSQL, Drizzle ORM, schema modeling for complex domains, query optimization, migrations
- Experience building scalable, distributed systems in cloud environments (AWS / Azure)
- Working knowledge of CI/CD, Docker, Kubernetes
- Familiarity with Tailwind CSS, Radix UI and modern component\-driven UI development
- High agency — you operate independently in ambiguous environments, take ownership of problems, and drive them to completion
- Strong problem\-solving and analytical skills
- Ability to work in a fast\-paced, evolving environment
- Communicate effectively with both technical and non\-technical stakeholders
Nice to have
- Experience building agentic coding tools, AI agent frameworks, or developer\-facing SDKs/APIs (Claude Agent SDK, OpenAI Agents SDK)
- Experience with Vercel ecosystem — Next.js, AI SDK providers, Turbopack
- Background in evaluation frameworks — measuring model capabilities, collecting human feedback at scale, A/B testing outputs
- Experience with sandboxed execution environments for safely running AI\-generated code
- Built research tools, experimentation platforms, or scientific software
- Proficiency with Python — FastAPI/Django, data pipelines, ML tooling
- Knowledge of observability tools (Grafana, Prometheus, Sentry, etc.)
- Experience building automation or internal platforms
- Familiarity with real\-time features — WebSockets, streaming UX, collaborative interfaces
- Knowledge of advanced web technologies — WebGL, WebAssembly, web workers, PWAs
- Experience with alternate JS runtimes — Bun, Deno
- Built open\-source tools or platforms with active user communities
- Strong quantitative foundation (math, physics, or related fields)
Representative Projects
Things you might build in this role:
- Interfaces for collecting and managing human feedback on model outputs at scale
- Experiment orchestration platforms — launch, monitor, and analyze complex AI research runs
- Visualization tools that help understand model behavior and identify failure modes
- Reusable components and frameworks that enable rapid development of new AI applications
- Sandboxed execution environments for safely running AI\-generated code
- AI\-powered personalization engines — tutoring, content generation, adaptive features
- Workflow builders that let non\-engineers orchestrate AI capabilities visually
- Enterprise integrations — ServiceNow, Salesforce, Confluence, Jira
*About NiCE*
*NICE Ltd. (NASDAQ: NICE) software products are used by 25,000\+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3\+ billion financial transactions.*
*Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30\+ countries.*
*NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.*
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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At NiCE, 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 $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
NiCE AI Hiring
NiCE has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, AI Software Engineer, Data Engineer. Positions span Remote, US, Hoboken, NJ, US, Atlanta, GA, US.
Location Context
AI roles in Seattle pay a median of $223,600 across 678 tracked positions. That's 22% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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