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About This Role
About Us
At Litify, we're revolutionizing the Legal industry by being the platform powering legal's top performers. As a trailblazer in legal technology, Litify delivers an all\-in\-one legal operating solution that empowers law firms and legal departments to achieve consistent success by continually standardizing, measuring, and improving their legal operations.
Our mission is clear: to deliver better business outcomes to our clients, so they can focus on delivering the best legal service and outcomes to their clients. 400\+ enterprise businesses and 55K\+ legal professionals trust Litify to amplify their impact with innovative technology and service that stands the test of time.
Backed by Bessemer Venture Partners, Litify is proud to be recognized as one of Inc. 5000 and Deloitte Technology Fast 500's fastest\-growing private companies in America along with numerous awards for our unparalleled software. With offices in the vibrant cities of New York and New Orleans, we're at the heart of legal innovation.
About the Role
Litify is looking for a Senior Software Engineer to join our Engineering team! As an Engineer, your main focus will be to write clean and scalable code to run on AWS services (e.g. Lambda, Fargate) and Salesforce. You will also assist with the design, analysis, development, implementation, and deployment of new data\-driven applications. The ideal candidate has the ability to function in an agile software development lifecycle and will thrive in a hands\-on, focused, productive and team\-oriented environment.
In this role, you will:
- Write scalable and maintainable Javascript for AWS services (e.g. Lambda, Fargate)
- Design, develop and monitor RESTful APIs
- Utilize AWS to create cost effective and efficient services for file migrations
- Create clean and organized Cloud Formations Templates and deploy stacks to hundreds of customers
- Manage the security and organization of millions of files in S3
To be successful in this role, you have (required):
- 7\+ years of experience with a backend programing language (preferably Node.js)
- 3\+ years experience working in a cloud environment (preferably AWS)
- Experience with AI code assistance tooling such as Github Co\-pilot, Claude Code, Etc
- A drive to continue to improve your skillset and take on more challenging projects
- A willingness to help educate and grow teammates
- An ability to effectively communicate with peers in Engineering and Product
Nice\-to\-have:
- Familiarity with AWS or Salesforce
- 3\+ years of Node.js experience
- Serverless architecture on AWS (API Gateway, Lambda, DynamoDB, S3\)
- AWS certifications, especially "AWS Certified Developer \- Associate"
- Networking experience
- Cybersecurity experience
- CI/CD experience
- Multi\-account AWS account architecture
- AWS CDK experience
- AWS CloudFormation experience
- Electron experience
Disclosure:
The estimated base salary pay range for this role is $135,000\-145,000\. You may also be offered a bonus and benefits.
Our salary ranges are based on paying competitively for our size and industry, and are one part of many compensation, benefits and other reward opportunities we provide.
Individual pay rate decisions are based on a number of factors, including qualifications for the role, experience level, skill set, and balancing internal equity relative to peers at the company.
The range above is for the expectations as laid out in the job description, however we are often open to a wide variety of profiles, and recognize that the person we hire may be less experienced (or more senior) than this job description as posted. If that ends up being the case, the updated salary range will be communicated to you as a candidate.
Ready to make a difference with us? Discover more about Litify and explore our open roles at www.litify.com. Connect with us on Instagram (@LitifyHQ), Twitter (@LitifyHQ), or LinkedIn.
Salary Context
This $135K-$145K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 219 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 4,021 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Litify, 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 $234,000 based on 758 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 40% below the category median. Disclosed range: $135K to $145K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Litify AI Hiring
Litify has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $110K - $145K.
Location Context
AI roles in New York pay a median of $210,300 across 2,585 tracked positions. That's 5% 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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