Interested in this AI Software Engineer role at Indexnine Technologies?
Apply Now →Skills & Technologies
About This Role
Why Indexnine?
------------------
We believe that great people build great products. That’s why we’ve created an environment where talent thrives.
### Health \& Wellness
Comprehensive health, dental, and vision insurance, plus wellness programs and mental health support.
### Work\-Life Balance
Flexible working hours, remote work options, and unlimited PTO to maintain a healthy work\-life balance.
### Professional Growth
Continuous learning opportunities, conference attendance, and skill development programs.
### Collaborative Culture
Work with talented, passionate people in an inclusive and supportive environment.
### Competitive Compensation
Competitive salaries, equity participation, and performance\-based bonuses.
### Innovation \& Impact
Innovation\-driven projects with new technologies, open\-source contributions, and real\-world impact.
We are looking for AI\-native Engineers who can build applications using AI and full\-stack
technologies, leveraging modern tools like Copilots, Agents, and LLMs to deliver production\-ready solutions.
You will work in an environment where:
- Code is co\-created with AI
- Specs are translated into working systems using AI tools
- Speed, quality, and product thinking matter equally
### Responsibilities
- Build and scale AI\-native applications from prototype to production
- Translate PRDs into working systems using AI\-assisted development
- Design and develop full\-stack solutions (frontend, backend, APIs)
- Integrate LLMs, RAG pipelines, and embeddings into real\-world use cases
- Leverage AI tools (Copilot, Cursor, Claude) to accelerate development
- Optimize performance, cost, and reliability
### Requirements
Programming \& Backend
- Python (preferred), Node.js / JavaScript
- Experience building APIs (REST/GraphQL)
Frontend
- React or similar frameworks
- Basic UI/API integration understanding
AI / LLMs
- LLM APIs (OpenAI, Anthropic)
- Prompt engineering basics
- RAG and embeddings exposure
- Interaction with agents \& agentic workflow (Nice to have)
Tools
- Copilot, Cursor, Claude
- LangChain, LlamaIndex (Nice to have)
Cloud
- AWS / GCP / Azure basics
- Deployment and debugging awareness
Core Skills
- Product thinking and user\-focused development
- AI\-assisted development workflow
- Strong full\-stack fundamentals
- LLM integration and debugging
- AI workflows and automation understanding
- Performance, cost, and reliability awareness
### Experience Bands
1–3 Years
- Use AI tools for development and debugging
- Basic prompt engineering and output evaluation
- Build small AI features and API integrations
3–6 Years
- Build end\-to\-end features independently
- Work on RAG and AI workflows in production
- Improve system performance and reliability
6–8 Years
- Design AI systems and architectures
- Handle ambiguity and lead technical decisions
- Mentor team and define best practices
### Nice to Have
- Experience working with agent\-based systems and agentic workflows
- Built copilots, internal tools, or automation\-driven applications using AI
- Exposure to fast\-paced product or startup environments
- Experience in building benchmarking frameworks for LLMs (A/B testing, offline and end\-to\-end evaluation) to measure quality, latency, and cost trade\-offs
- Understanding of LLM proxy patterns including routing, caching, safety filters, and rate limiting across multiple models/vendors
### Qualifications
- B.E / B.Tech / M.Tech / MCA
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 Indexnine 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 $235,100 based on 665 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
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.
Indexnine Technologies AI Hiring
Indexnine Technologies has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Wilmington, DE, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.