Interested in this AI Software Engineer role at Epiq?
Apply Now →Skills & Technologies
About This Role
At Epiq , your work contributes to complex, global legal outcomes. You’ll join a values‑driven community where integrity guides decisions, relentless service sets the bar, and we thrive on big challenges together. We invest in your growth with enterprise‑wide learning and mobility. We celebrate who you are, and we respect life beyond work with flexibility that’s recognized externally. Enabled by modern platforms and AI, you’ll do the most meaningful work of your career and see your impact at scale.
Job Description:
Epiq AI Labs is the innovation and engineering hub behind Epiq’s next\-generation AI platform for corporate legal departments and global law firms. Operating with the speed and autonomy of a startup and the resources of a global alternative legal services provider, the team builds intelligent agents, reasoning engines, knowledge systems, and structured workflows for litigation, investigations, compliance, and corporate knowledge work.
The team is highly collaborative, deeply technical, and focused on rapid iteration, thoughtful design, and end\-to\-end ownership.
The Opportunity
You will build the cloud infrastructure beneath Epiq AI Labs’ AI platform, spanning infrastructure as code, Kubernetes, CI/CD and release engineering, networking, secrets management, observability, security, and compliance infrastructure. Ownership extends from initial design through production operation.
Infrastructure is treated as a product whose reliability, scalability, and security shape the capabilities of the entire platform. You will partner closely with backend engineering, AI engineering, product management, and security to establish deployment, observability, and security patterns that can scale with the organization . This role will be in the office 3\~4 days a week.
Essential Job Responsibilities
- Design and implement cloud infrastructure using Terraform, including reusable modules, environment topology, and drift detection and remediation.
- Operate Kubernetes in production, including orchestration, autoscaling, resource governance, network policy, and cluster lifecycle management.
- Build and maintain CI/CD and release infrastructure with progressive delivery, rollback mechanisms, and efficient paths from merge to production.
- Define platform service\-level objectives and build the metrics, tracing, alerting, and error\-budget practices required to support them.
- Implement security and compliance infrastructure, including hardening, audit logging, data\-residency controls, retention, legal hold, and audit evidence collection.
- Build and maintain network, secrets, key, credential, TLS, and certificate\-lifecycle infrastructure.
- Contribute to incident response, post\-incident review, developer tooling, technical design documentation, architectural review, and platform operational readiness.
Required Qualifications
- 3\+ years of experience in infrastructure engineering, platform engineering, or site reliability engineering.
- Demonstrated experience building and operating production infrastructure, including on\-call responsibility for systems of your own design.
- Hands\-on experience with at least one major cloud platform such as AWS, GCP, or Azure.
- Experience with infrastructure\-as\-code tools, particularly Terraform, including reusable module design.
- Production Kubernetes experience, including scaling, upgrades, resource limits, network policy, and troubleshooting.
- Ownership of CI/CD pipelines using GitHub Actions, Azure DevOps, or a comparable platform.
- Experience with observability tooling such as Prometheus, Grafana, or OpenTelemetry, including defining and maintaining service\-level objectives.
- Demonstrated incident\-command experience in production environments.
- Experience with secrets and certificate management at organizational scale.
- Proficiency in Python, Go, or a comparable language sufficient to build tooling and automation.
- Strong system\-design and architecture experience, including production of technical design documents.
Technology Stack
· Azure · Terraform · Kubernetes · Docker · CI/CD platform to be confirmed · Prometheus · Grafana · OpenTelemetry · PostgreSQL · RabbitMQ · Python.
\#LI\-KS1
The Compensation range for this role is $145,000 \-$195,000 USD annually and may be eligible for an annual bonus.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
Must be authorized to work in the United States for any employer.
Your specific salary will be determined based on several factors:
- Location\-based market rate for the role
- Your abilities in relation to the job specification
- Performance during screening and interview
- Pay parity with the wider team in the considered location
Further details about the package will be provided during the initial screening call with the Talent Acquisition Team.
Epiq Leadership Compass
Fosters Relationships \& Collaboration
Builds trust and alignment through open communication, shared goals, and strong partnerships to drive collective success.
- Build trust\-based partnerships
- Nurture long\-term relationships
- Remove collaboration barriers
- Celebrate cross\-team success
Engages \& Influences
Inspires action and alignment through clear communication, purposeful influence, and a compelling vision.
- Use storytelling to build buy\-in
- Align communication with organizational goals
- Guild alignment through strong engagement
Maximizes Performance
Sets and reinforces performance standards that drive results, ensure accountability, and align with Epiq’s goals.
- Use data to identify improvement opportunities
- Make informed decisions
- Align team goals with boarder strategy
- Empower teams to manage their own goals
- Translate vision into clear priorities
- Prepare for disruptions with strong change management
Achieves Operational Success
Drives continuous improvement and operational excellence through smart processes, data insights, and quality execution.
- Improve workflows for team efficiency
- Use clear documentation and expectations
- Resolve issues quickly using data and feedback
It is Epiq’s policy to comply with all applicable equal employment opportunity laws by making all employment decisions without unlawful regard or consideration of any individual’s race, religion, ethnicity, color, sex, sexual orientation, gender identity or expressions, transgender status, sexual and other reproductive health decisions, marital status, age, national origin, genetic information, ancestry, citizenship, physical or mental disability, veteran or family status or any other basis protected by applicable national, federal, state, provincial or local law. Epiq’s policy prohibits unlawful discrimination based on any of these impermissible bases, as well as any bases or grounds protected by applicable law in each jurisdiction. In addition Epiq will take affirmative action for minorities, women, covered veterans and individuals with disabilities. If you need assistance or an accommodation during the application process because of a disability, it is available upon request. Epiq is pleased to provide such assistance and no applicant will be penalized as a result of such a request. Pursuant to relevant law, where applicable, Epiq will consider for employment qualified applicants with arrest and conviction records.
Salary Context
This $145K-$195K range is below the median for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Epiq, 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 $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($170K) sits 22% below the category median. Disclosed range: $145K to $195K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Epiq AI Hiring
Epiq has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in New York, NY, US. Compensation range: $195K - $255K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.