Interested in this AI/ML Engineer role at Navitus Health Solutions / Lumicera Health Services?
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About This Role
Company: Navitus About Us: Navitus \- Putting People First in Pharmacy \- Navitus was founded as an alternative to traditional pharmacy benefit manager (PBM) models. We are committed to removing cost from the drug supply chain to make medications more affordable for the people who need them. At Navitus, our team members work in an environment that celebrates diversity, fosters creativity and encourages growth. We welcome new ideas and share a passion for excellent service to our customers and each other.\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_. Current associates must use SSO login option at https://employees\-navitus.icims.com/ to be considered for internal opportunities. \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_We are committed to providing equal employment opportunity to all applicants and employees and comply with all applicable nondiscrimination regulations, including those related to protected veterans and individuals with disabilities. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, or handicap. Pay Range: USD $105,271\.00 \- USD $150,000\.00 /Yr. STAR Bonus % (At Risk Maximum): 5\.00 \- Salaried Non\-Management except pharmacists Work Schedule Description (e.g. M\-F 8am to 5pm): M\-F: 8:00 am – 5:00 pm Remote Work Notification: ATTENTION: Navitus is unable to offer remote work to residents of Alaska, Hawaii, Maine, Mississippi, New Hampshire, New Mexico, North Dakota, Rhode Island, South Carolina, South Dakota, West Virginia, and Wyoming. Overview:
Due to growth, we are adding a Engineer, Enterprise AI to our team!
The Engineer, Enterprise AI is responsible for designing, building, and operationalizing enterprise AI capabilities that enable the scalable adoption of Artificial Intelligence and Agentic AI across the organization. Working closely with the Enterprise AI Architect, this role translates enterprise AI architecture, patterns, and governance standards into implementable solutions and reusable technical components while partnering with Enterprise Architecture, Platform Engineering, Data Engineering, and Product teams to develop AI services, integrations, and operational frameworks that support generative AI, machine learning, and agentic workflows. The Engineer ensure AI solutions are implemented in alignment with enterprise architecture standards, security requirements, and regulatory compliance while enabling teams across the organization to safely and effectively leverage AI technologies to deliver business value.
Is this you? Find out more below!
Responsibilities:
How do I make an impact on my team?
- Design, build, and maintain enterprise AI solutions and services that enable the scalable adoption of generative AI, machine learning, and agentic AI capabilities across the organization.
- Implement enterprise AI architecture patterns and technical standards defined by the Enterprise AI Architect, translating reference architectures and design patterns into production\-ready solutions.
- Develop and support AI integration patterns that connect AI capabilities with enterprise applications, APIs, data platforms, and cloud services.
- Build and maintain AI\-enabled services and components, including model interfaces, orchestration services, agent frameworks, and reusable AI tooling for enterprise teams.
- Support the implementation of Agentic AI workflows, including orchestration, tool integration, context management, and human\-in\-the\-loop capabilities.
- Implement and maintain AI operational capabilities (MLOps/LLMOps) including model deployment pipelines, monitoring, observability, lifecycle management, and operational controls.
- Ensure AI solutions align with enterprise AI governance, responsible AI standards, and regulatory requirements, including data privacy, auditability, and model transparency.
- Support AI platform development and enablement, working with Platform Engineering and Data Engineering teams to deploy, scale, and maintain enterprise AI infrastructure and tooling.
- Other duties as assigned.
Qualifications:
What our team expects from you?
- Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, or a related technical field, or equivalent work experience, required.
- Certification/Licenses:
+ AWS Certified Solutions Architect – Associate, AWS Machine Learning – Specialty, or equivalent cloud\-based AI/ML certification preferred.
+ MLOps, AI platform engineering, or machine learning engineering certification preferred.
+ Responsible AI, AI governance, or AI ethics training/certification preferred.
- 6\-8 years of experience in software development, platform engineering, or machine learning engineering, including a minimum of 2\-4 years of hands\-on experience developing, integrating, or operationalizing Artificial Intelligence and Machine Learning solutions in production environments, required.
- Experience building or supporting AI\-enabled applications, data pipelines, model integrations, or AI platform components within cloud or distributed systems environments required.
- Experience implementing AI integration patterns that connect AI capabilities with enterprise applications, APIs, data platforms, and cloud services required.
- Experience developing or supporting generative AI and agentic AI workflows, including orchestration frameworks, tool integrations, context management, Model Context Protocol (MCP) servers or similar context services, and human\-in\-the\-loop patterns to support governed AI decision\-making, required.
- Experience implementing or supporting AI operational practices (MLOps/LLMOps) including model deployment pipelines, monitoring, observability, lifecycle management, and operational controls, required.
- Experience working within enterprise architecture standards and development frameworks, ensuring solutions aligned with architectural guardrails, security requirements, and platform standards required.
- Experience supporting AI governance and responsible AI practices, including data privacy, model transparency, auditability, and compliance with enterprise and regulatory standards required.
- Experience collaborating with architecture, engineering, data, and product teams to design and implement scalable AI\-enabled solutions required.
- Experience developing and supporting cloud\-based services, APIs, and distributed systems that enable scalable AI capabilities and enterprise platform integrations required.
- Strong development experience in modern programming languages, including Python, C\#, or similar backend languages used for AI services and integrations required.
- Proven ability to interview end\-users for insight on functionality, interface, problems, and/or usability issues required.
- Knowledge of PBM systems, claims adjudication processes, and data exchange patterns between payers, providers, and pharmacies preferred.
- Participate in, adhere to, and support compliance program objectives.
- The ability to consistently interact cooperatively and respectfully with other employees.
What can you expect from Navitus?
- Top of the industry benefits for Health, Dental, and Vision insurance
- 20 days paid time off
- 4 weeks paid parental leave
- 9 paid holidays
- 401K company match of up to 5% \- No vesting requirement
- Adoption Assistance Program
- Flexible Spending Account
- Educational Assistance Plan and Professional Membership assistance
- Referral Bonus Program – up to $750!
\#LI\-Remote
Location : Address: Remote Location : Country: US
Salary Context
This $105K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Navitus Health Solutions / Lumicera Health Services, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($127K) sits 41% below the category median. Disclosed range: $105K to $150K.
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.
Navitus Health Solutions / Lumicera Health Services AI Hiring
Navitus Health Solutions / Lumicera Health Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $150K - $150K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 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).
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 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
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