Interested in this AI Software Engineer role at Cotiviti?
Apply Now →Skills & Technologies
About This Role
Overview:
Cotiviti is a leading solutions and analytics company that leverages unparalleled clinical and financial datasets to deliver deep insight into the performance of the healthcare system. These insights uncover new opportunities for healthcare organizations to collaborate to improve their financial performance, reduce inefficiency, and improve healthcare quality.
The Senior Software Engineer I (AI Orchestrator) is a senior individual contributor responsible for building production software using AI‑native development workflows as part of a small, focused AI‑Native team. This role works closely with the AI‑Native Tech Lead and other team engineers to rapidly design, implement, test, and iterate on high‑quality software.
AI Orchestrators are strong engineers who use AI tools as a first‑class part of their daily engineering workflow to accelerate development, improve testing, and shorten feedback loops.
This role is not focused on training AI models or building machine learning pipelines. It focuses on building software efficiently, safely, and at high quality using AI‑assisted engineering practices.
Responsibilities:
AI‑Native Software Development* Build and enhance production‑grade software applications using AI‑assisted development workflows.
- Translate product and technical requirements into working, maintainable software.
- Use AI tools to accelerate coding, debugging, testing, refactoring, and documentation.
- Review, validate, and refine AI‑generated code to ensure correctness, performance, security, and maintainability.
Application Design \& Implementation* Design and implement clean, well‑structured APIs, services, and application components.
- Contribute to scalable backend systems and full‑stack solutions as required by the team.
- Apply sound engineering judgment to balance speed, quality, and long‑term maintainability.
- Participate in design discussions and technical reviews within the team.
AI Orchestration \& Workflow Execution* Orchestrate the effective use of AI tools across development activities, including:
+ Code generation and refactoring
+ Test creation and validation
+ Debugging and issue analysis
+ Documentation and design artifacts
- Follow and help refine AI‑native engineering standards established by the Tech Lead.
- Contribute feedback and improvements to AI‑assisted workflows and development practices.
Collaboration \& Team Execution* Collaborate closely with the AI‑Native Tech Lead to implement architectural and engineering best practices.
- Work effectively within a small AI‑Native team to deliver features end‑to‑end.
- Coordinate with adjacent teams as needed for integration, dependencies, and shared services.
- Communicate technical issues and tradeoffs clearly to engineering and product stakeholders.
Quality, Stability \& Continuous Improvement* Ensure adherence to Cotiviti technology standards, secure coding practices, and SDLC requirements.
- Analyze and resolve software issues originating from internal or external customers.
- Execute enhancements to improve system performance, reliability, and availability.
- Continuously improve knowledge of new tools, technologies, and AI‑assisted development practices.
Complete all responsibilities as outlined on annual Performance Plan.
Complete all special projects and other duties as assigned.
Must be able to perform duties with or without reasonable accommodation.
Qualifications:
- BS or MS Computer Science, Information Technology, Information Systems, or equivalent work experience.
- 5\+ years of professional software engineering experience.
- Strong backend or full‑stack development skills.
- Demonstrated experience building and shipping production applications.
- Comfortable using AI‑assisted development tools as part of daily engineering workflow.
- Strong debugging, problem‑solving, and code review skills.
- Proficiency in one or more programming languages such as Java, .NET, Python, or similar.
- Experience working with relational databases and SQL.
- Familiarity with CI/CD pipelines and modern development practices.
- Good written and verbal communication skills.
- Strong collaboration and teamwork skills
- Ability to learn and adapt quickly in a rapidly evolving technical environment.
- Detail\-oriented approach to software quality and correctness.
- Strong sense of ownership for delivered outcomes.
- Proficiency working with large data sets.
Preferred Qualifications* Experience working in fast‑paced product or platform teams.
- Experience building internal tools, automation, or developer utilities.
- Experience with cloud platforms such as AWS, Azure, or OCI.
- Experience with containerization technologies (Docker, Kubernetes).
- Interest in improving developer productivity and engineering workflows
Mental Requirements:* Critical Thinking: Ability to think critically and evaluate information objectively, considering different perspectives and potential implications before drawing conclusions or making recommendations.
- Attention to Detail: must have a keen eye for detail to ensure accuracy in data analysis, interpretation, and reporting.
- Quantitative Aptitude: Strong numerical skills are essential for conducting quantitative analysis, working with statistical methods and models, and manipulating data using mathematical operations.
- Data Interpretation: skilled in interpreting data visualizations, charts, graphs, and other forms of data presentation to extract meaningful insights and communicate findings effectively.
- Communication Skills: Effective communication skills are crucial for conveying complex technical concepts and insights to non\-technical stakeholders clearly and understandably through written reports, presentations, and verbal discussions.
- Curiosity and Learning Agility: A strong desire to learn and explore new methodologies, techniques, and tools in the field of data analysis and insights generation is essential for staying current with industry trends and best practices.
- Resilience: The ability to handle pressure, adapt to changing priorities, and overcome setbacks is important in a fast\-paced and sometimes ambiguous analytical environment.
- Ethical and Integrity: Upholding ethical standards and maintaining integrity in handling sensitive data and information is paramount for building trust and credibility in the insights provided.
Physical Requirements and Working Conditions:* Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands, and/or fingers.
- Must be able to provide a dedicated, secure work area.
- be able to provide high\-speed internet access/connectivity and office setup and maintenance.
- No adverse environmental conditions expected.
*Base compensation ranges from $105,000 to $145,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.* *Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17\-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our* *Careers page.* *This* *role is based remotely and all interviews will be conducted virtually.*
Date of posting: 03/16/2026
Applications are assessed on a rolling basis. We anticipate that the application window will close on 06/15/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.
\#LI\-REMOTE
\#LI\-RA1
\#senior
Salary Context
This $105K-$145K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $189K across 518 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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At Cotiviti, 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. This role's midpoint ($125K) sits 47% below the category median. Disclosed range: $105K to $145K.
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.
Cotiviti AI Hiring
Cotiviti has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety, AI Software Engineer. Based in Remote, US. Compensation range: $124K - $180K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 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.