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About This Role
Overview:
Staff Engineer I (AI, Java) is a senior hands\-on technical leader responsible for designing and building intelligent backend services and workflow\-integrated solutions within an AI\-native development environment. This role combines deep expertise in Java\-based systems and workflow orchestration with AI\-assisted engineering (Claude, Copilot) to enable scalable automation, intelligent routing, and decisioning.
The role partners with Technical Architects and leads engineering teams to deliver secure, scalable, and reusable services across enterprise workflow platforms.
Responsibilities:
- Lead design and development of Java\-based backend services (Spring Boot, microservices)
- Implement solutions across workflow platforms (Temporal, Camunda, Flowable, JBPM, Drools, Appian)
- Build AI\-enabled service capabilities for workflow automation and decision support
- Drive adoption of AI\-assisted development (Claude, Copilot) with strong validation and engineering standards
- Implement AI\-native patterns:
- + intelligent routing
+ context\-aware processing
+ embedded decision support
- Lead microservices and event\-driven architectures for scalable systems
- Collaborate with architects to translate design into implementation
- Provide technical leadership:
- + design/code reviews
+ mentoring engineers
+ guiding best practices
- Support CI/CD, DevSecOps, and cloud deployments (OpenShift, Kubernetes, AWS
- Drive reusable services and shared platform components
Qualifications:
- Bachelor’s degree in Computer Science or equivalent
- 8–10\+ years of enterprise backend development experience
- Strong expertise in:
- + Java, Spring Boot, microservices
+ REST APIs and integration patterns
+ Workflow orchestration platforms
- Experience integrating AI\-driven capabilities into enterprise systems
- Strong understanding of distributed systems and event\-driven architecture
- Experience with:
- + PostgreSQL / Oracle
+ CI/CD, DevOps, OpenShift, Kubernetes, AWS
- Familiarity with Copilot, Claude or similar tools
- Proven ability to lead design, mentor engineers, and drive alignment
Preferred Qualifications* Experience building workflow and decision automation platforms
- Exposure to AI\-native development practices
- Experience with Appian or similar platforms
- Background in Healthcare IT or regulated environments
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 $145,000 to $176,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.* *This role is eligible for discretionary bonus consideration.* *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: 00/00/2026
Applications are assessed on a rolling basis. We anticipate that the application window will close on 00/00/2026, 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
Salary Context
This $145K-$176K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Cotiviti, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 11% below the category median. Disclosed range: $145K to $176K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cotiviti AI Hiring
Cotiviti has 9 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, Data Engineer. Based in Remote, US. Compensation range: $54K - $258K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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