Interested in this AI/ML Engineer role at Claritev?
Apply Now →Skills & Technologies
About This Role
At Claritev, we pride ourselves on being a dynamic team of innovative professionals. Our purpose is simple \- we strive *to* *bend the cost curve in healthcare*for all. Our dedication to service excellence extends to all our stakeholders – internal and external \- driving us to consistently exceed expectations. We are intentionally bold, we foster innovation, we nurture accountability, we champion diversity, and empower each other to illuminate our collective potential.
Be part of our amazing transformational journey as we optimize the opportunity towards becoming a leading technology, data, and innovation voice in healthcare. Onward and Upward!!!
JOB SUMMARY:As the Senior AI Ops Engineer , you will lead the advancement of our AI capabilities, focusing on engineering excellence and leveraging generative AI to enhance our end\-to\-end operations from experimentation to deployment. Your role will involve driving research and development in analytical techniques, creating new AI\-driven products, and mentoring junior talent. With our infrastructure transitioning to Oracle Cloud Infrastructure (OCI) , you will play a pivotal role in architecting, optimizing, and scaling AI workloads within OCI’s ecosystem. Join us in transforming the healthcare landscape! *Two Openings!!!*
JOB ROLES AND RESPONSIBILITIES
Enhance AI Operations
- Utilize generative AI to streamline processes and tools, enabling faster and more effective development and deployment.
- Build scalable GenAI infrastructure with Terraform, Docker, Helm, and Kubernetes (OKE, AKS, and OCI\-native services such as OCI Data Science, OCI AI Infrastructure, and OCI Streaming).
- Architect hybrid AI systems across OCI, Azure, and on\-prem GPU clusters, ensuring seamless data and model orchestration.
Automate model pipelines (training fine\-tuning* inference) using Jenkins, GitHub Actions, and MLflow integrated with OCI Object Storage and OCI DevOps.
- Serve multimodal LLMs with vLLM, Triton, and Streamlit dashboards deployed on OCI Container Engine for Kubernetes (OKE) and optimized for OCI GPUs.
- Deploy secure RAG\-based microservices backed by Vector DBs, Databricks, and OCI AI Services (e.g., Data Labeling, Generative AI APIs) and private LLM gateways.
Lead R\&D Efforts
- Drive research and development using generative AI to create reusable code components and establish best practices for OCI\-based MLOps and AI infrastructure .
Stakeholder Collaboration
- Partner with stakeholders to identify opportunities for new AI\-driven products through data insights, leveraging OCI’s data and analytics stack (Autonomous Data Warehouse, Data Flow, Data Integration).
Program\-Level Initiatives
- Lead initiatives focused on the initial development of AI\-powered products that utilize OCI\-native AI and ML services for scalability and compliance.
Ethical AI/ML Practices
- Ensure AI models are transparent, relevant, and ethically used across all environments, maintaining consistency between OCI and multi\-cloud systems.
Team Management
- Mentor and develop junior data scientists and engineers, fostering a culture of continuous learning and innovation in OCI and multi\-cloud AI engineering .
Compliance
- Ensure adherence to HIPAA regulations and data privacy standards: HIPAA, SOC2, BAA enforcement, and PHI\-safe model routing within OCI’s secure architecture .
Requirements: JOB REQUIREMENTS (Education, Experience, and Training):
Must have 7\+ years in Data Science, 3\+ years in R\&D, 2\+ years mentoring/managing junior engineers.
Deep expertise in Kubernetes, CI/CD, LLMOps, and cloud\-native architecture. Exposure to taking AI agents from concept to compliance\-ready production is preferred.
Strong ML Infra experience with hands\-on Terraform/K8s/IaC.
Deep experience with LLMOps, model versioning, and hybrid GPU deployment.
Must have fluency in Python, Bash, and container\-native toolchains.
Familiarity with open\-weight LLMs (Mistral, Grok, LLaVA, etc.)
Passion for building AI that’s not just powerful, but secure and compliant.
Experience in dealing with Peta Bytes of data.
Strong problem\-solving, critical thinking, creativity, organizational, and communication skills.
Preferred Experience : Healthcare and process optimization.
Bachelor's in Quantitative Science; Master's or PhD preferred.
COMPENSATION
The salary range for this position is $170K to $180K. Specific offers take into account a candidate’s education, experience and skills, as well as the candidate’s work location and internal equity. This position is also eligible for health insurance, 401k and bonus opportunity.
BENEFITS
We realize that our employees are instrumental to our success, and we reward them accordingly with very competitive compensation and benefits packages, an incentive bonus program, as well as recognition and awards programs. Our work environment is friendly and supportive, and we offer flexible schedules whenever possible, as well as a wide range of live and web\-based professional development and educational programs to prepare you for advancement opportunities.
Your benefits will include:
Medical, dental and vision coverage with low deductible \& copay
Life insurance
Short and long\-term disability
Paid Parental Leave
401(k) \+ match
Employee Stock Purchase Plan
Generous Paid Time Off – accrued based on years of service
WA Candidates: the accrual rate is 4\.61 hours every other week for the first two years of tenure before increasing with additional years of service
10 paid company holidays
Tuition reimbursement
Flexible Spending Account
Employee Assistance Program
Sick time benefits – for eligible employees, one hour of sick time for every 30 hours worked, up to a maximum accrual of 40 hours per calendar year, unless the laws of the state in which the employee is located provide for more generous sick time benefits.
EEO STATEMENT
Claritev is an Equal Opportunity Employer and complies with all applicable laws and regulations. Qualified applicants will receive consideration for employment without regard to age, race, color, religion, gender, sexual orientation, gender identity, national origin, disability or protected veteran status. If you would like more information on your EEO rights under the law, .
APPLICATION DEADLINE
We will generally accept applications for at least 5 calendar days from the posting date or as long as the job remains posted.
\#LI – PS1
Salary Context
This $170K-$180K 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 Claritev, 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. Disclosed range: $170K to $180K.
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.
Claritev AI Hiring
Claritev has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $210K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.