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About This Role
The AI Operations Engineer is responsible for the operational stability, reliability, and continuous performance of deployed AI models and services. The AI Operations Engineer ensures that AI solutions built are seamlessly operationalized, monitored, and maintained in production environments. By managing the lifecycle of AI applications, from automated deployment and infrastructure scaling to real-time monitoring and incident response, this role ensures that AI-driven capabilities remain secure, cost-efficient, and highly available to support Alliance Health’s mission-critical clinical and operational workflows.
This position is full-time hybrid. Selected candidate must come into the Home office in Morrisville on Wednesdays, for business meeting as needed, reside within 50 miles of Morrisville North Carolina.
Responsibilities & Duties
AI Model Deployment & Infrastructure Operations
- Deploy, monitor, support, and maintain AI models in production environments, ensuring seamless integration with existing enterprise systems
- Develop and manage automated CI/CD and MLOps pipelines for model packaging, testing, deployment, rollback, and full lifecycle management
- Architect and manage hybrid cloud-AI environments that support multi-model, multi-agent, and edge AI deployments
- Implement infrastructure automation using scripting, Infrastructure as Code (IaC), and configuration management to ensure scalable and resilient AI services
- Design and optimize AI infrastructure in collaboration with server, cloud, and platform engineering teams to support high-performance workloads
AI System Observability & Reliability
- Maintain comprehensive observability across all AI systems through advanced logging, metrics, performance monitoring, tracing, and alerting
- Detect and respond to critical operational risks, including model drift, performance degradation, and training-serving skew
- Define and monitor key operational metrics such as availability, latency, throughput, token consumption, model cost, and task-completion rates
- Provide ongoing technical support, including rapid incident response, troubleshooting, and root cause analysis for AI platforms and user-facing services
- Improve platform reliability, resilience, and cost-efficiency through continuous tuning, automation, and operational improvements
AI Governance, Security & Compliance
- Partner with Cybersecurity, Risk, and AI Governance teams to implement and evidence required security controls
- Implement threat-resistant AI infrastructure and enforce security frameworks to protect against data exfiltration and unauthorized access
- Ensure compliance with regulatory obligations and internal policies by maintaining model version control, audit trails, and lineage/traceability for data, prompts, and outputs
- Enforce data governance patterns, including privacy controls, data masking, and the secure management of secrets and identity across AI workflows
Operational Enablement & Collaboration
- Enable self-service platform consumption for AI Engineers and Data Scientists via standardized APIs, reusable pipelines, templates, and documentation
- Collaborate across divisions to align technical implementations with mission-critical business objectives and reduce operational disruption
- Communicate technical constraints such as latency, cost, and performance effectively to business stakeholders and technical leads
- Evaluate emerging AI tools, frameworks, and infrastructure approaches to determine their operational fit, supportability, and long-term value for the organization
- Develop and maintain essential technical documentation, including runbooks, architecture diagrams, and standard operational procedures
User and Technical Support & Troubleshooting
- Provide hands-on technical assistance to resolve issues arising from the integration of AI models into existing business workflows and systems
- Perform root cause analysis for technical failures in AI pipelines, including issues related to data integration, model performance, or infrastructure connectivity
- Support users in navigating technical complexities related to new AI platforms, tools, and orchestration frameworks to ensure minimal disruption to their operations
Minimum Requirements
Education & Experience
Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field from an accredited college or university, and three (3) years of experience developing, implementing, and deploying software, machine learning, and/or artificial intelligence solutions. Experience must include integrating, deploying, monitoring, and maintaining AI/ML models and/or third-party AI/ML tools within production environments for internal business operations and/or customer-facing applications.
At least two (2) years of experience utilizing Python and related machine learning and artificial intelligence libraries and frameworks to develop, test, deploy, and support AI/ML solutions is required.
Knowledge, Skills, & Abilities
- MLOps & Model Lifecycle Management: Automating model deployment, testing, versioning, and rollback processes (CI/CD for ML).
- LLMOps & Generative AI Operations: Managing prompt configurations, evaluation pipelines, and lifecycle management for LLMs.
- Infrastructure as Code (IaC) & Automation: Using tools like Terraform or Ansible to deploy and manage cloud-based AI infrastructure.
- Cloud & Hybrid Architecture: Managing compute resources and hybrid cloud environments (Azure) for AI workloads.
- Observability & Monitoring: Implementing logging, metrics, and alerting for model drift, latency, accuracy, and token consumption.
- Containerization & Orchestration: Docker and Kubernetes for scaling and managing AI microservices and agents.
- Programming & Scripting: Python, with familiarity with industry standard AI and ML libraries.
- AI Security & Governance: Implementing threat-resistant infrastructure, IAM, encryption, and compliance within AI workflows.
- Technical Troubleshooting & Support: Ability to perform root cause analysis for complex integration issues and provide high-level support to technical users.
- AI-Augmented Productivity: Ability to utilize AI tools (GitHub Copilot) to accelerate operational tasks and documentation.
Employment for this position is contingent upon a satisfactory background check and credit check, which will be performed after acceptance of an offer of employment and prior to the employee's start date.
Salary Range
$107,607-$137,198/Annually
Exact compensation will be determined based on the candidate's education, experience, external market data and consideration of internal equity.
An excellent fringe benefit package accompanies the salary, which includes:
- Medical, Dental, Vision, Life, Long Term Disability
- Generous retirement savings plan
- Flexible work schedules including hybrid/remote options
- Paid time off including vacation, sick leave, holiday, management leave
- Dress flexibility
Education
Required* Bachelors or better in Computer Science
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $107K-$137K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Alliance Health Plan, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $107K to $137K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Alliance Health Plan AI Hiring
Alliance Health Plan has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Morrisville, NC, US. Compensation range: $137K - $137K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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