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About This Role
Summary:
We’re building the future of intelligent systems—and we’re looking for an AI and Automation Engineer to help lead the charge. In this role, you’ll design, build, and deploy scalable automation solutions that drive efficiency, reduce manual work, and accelerate innovation across the organization.
You’ll work closely with teammates in Information Technology as well as business stakeholders to identify automation opportunities and turn them into reliable, AI\-driven workflows. From robotic process automation (RPA) to workflow orchestration and generative AI, your work will have a measurable impact in helping to shape Sarnova’s future.
Essential Duties and Responsibilities:
- Design, develop, deploy, and support AI agents using Azure AI Foundry and Microsoft Copilot Studio.
- Build conversational, autonomous, and multi\-agent solutions using the Foundry SDK, Agent Framework, Responses API, Copilot Studio, and related Microsoft AI technologies.
- Design prompt\-based and hosted agents, selecting and optimizing models based on business requirements, performance, quality, and cost.
- Integrate AI solutions with enterprise data and business systems through APIs, MCP, Power Platform, Dataverse, Dynamics 365, Azure Functions, custom services, and external platforms.
- Implement Retrieval\-Augmented Generation (RAG) and knowledge\-grounding solutions using Azure AI Search, Foundry IQ, and enterprise knowledge sources.
- Design and orchestrate multi\-agent workflows, Agent\-to\-Agent (A2A) coordination, routing, and human\-in\-the\-loop processes.
- Develop scalable, reusable, secure, and enterprise\-ready AI automation solutions.
- Monitor, troubleshoot, evaluate, and continuously improve AI solution quality, reliability, safety, and performance.
- Create technical documentation, testing assets, operational playbooks, and implementation standards.
- Collaborate with business and technical stakeholders to identify, prioritize, and deliver AI\-driven solutions that provide measurable business value.
- Stay current with emerging AI technologies and advancements across Azure AI Foundry, Copilot Studio, Power Platform, and the Microsoft AI ecosystem.
Skills/Experience Required:
- 2\+ years of experience in AI development, automation engineering, software development, or a related field.
- Hands\-on experience designing, developing, deploying, and supporting AI agents using Azure AI Foundry and Microsoft Copilot Studio.
- Experience with agent development and orchestration, including prompt\-based and hosted agents, Foundry SDK, Agent Framework, Responses API, multi\-agent workflows, and agent integrations.
- Experience implementing Retrieval\-Augmented Generation (RAG) and knowledge\-grounding solutions using Azure AI Search, vector search, embeddings, and citation\-based responses.
- Experience building conversational and autonomous agents in Copilot Studio, including generative answers, knowledge grounding, and workflow automation.
- Experience integrating AI solutions with Power Platform, Dataverse, Dynamics 365, APIs, Azure Functions, microservices, and enterprise applications.
- Proficiency in Python (preferred), C\#, and/or JavaScript/TypeScript, along with modern development tools such as VS Code, Git, Azure DevOps, and Azure CLI.
- Knowledge of Model Context Protocol (MCP), custom function tools, external integrations, and API\-driven architectures.
- Strong understanding of leading LLMs, prompt engineering, context management, conversational memory, model evaluation, and AI solution optimization.
- Experience with Azure services including Azure Functions, Azure AI Search, Storage, Key Vault, Microsoft Entra ID, and microservice\-based solutions.
- Working knowledge of SQL Server, data integration concepts, SharePoint, and Dynamics 365 Finance \& Operations and CRM applications.
- Familiarity with observability, monitoring, application logging, security, Responsible AI practices, governance, and CI/CD deployment processes.
- Experience delivering automation solutions using Power Automate, UiPath, or similar workflow and automation platforms.
- Strong analytical, communication, and stakeholder collaboration skills with the ability to translate business requirements into scalable technical solutions
Preferred Qualifications:
- Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Technology, or a related field; equivalent practical experience will also be considered.
- Microsoft certifications related to Azure AI, AI Engineering, Power Platform, Copilot Studio, or Agent Development (e.g., Azure AI Engineer Associate or successor certifications).
- Experience in healthcare, regulated, distribution, manufacturing, or other compliance\-focused industries with an understanding of security, privacy, and governance requirements.
Experience with one or more of the following advanced Microsoft AI capabilities is highly desirable:
- Deploying and managing hosted agents in Azure AI Foundry, including managed runtimes, identity, observability, and enterprise governance.
- Designing multi\-agent architectures, Agent\-to\-Agent (A2A) coordination, autonomous workflows, and human\-in\-the\-loop processes.
- Developing and maintaining MCP servers, MCP tool catalogs, reusable agent tools, and custom function integrations.
- Leveraging Foundry IQ, Azure AI Search, and advanced RAG architectures for enterprise knowledge grounding and retrieval.
- Utilizing advanced Azure AI Foundry capabilities such as built\-in agent tools, managed memory, evaluation frameworks, observability, guardrails, and governance controls.
- Publishing and managing agents across Microsoft Teams, Microsoft 365 Copilot, Dynamics 365, and custom web experiences.
- Implementing enterprise AI governance, DLP policies, ALM strategies, environment management, and Responsible AI frameworks.
- Experience integrating AI solutions with Microsoft Fabric, Power BI, advanced analytics platforms, or enterprise data ecosystems.
- Knowledge of Microsoft Entra Agent ID, managed identities, enterprise authentication, and secure agent access patterns.
Physical Requirements:
- Sit, walk, stand, use hands to manipulate, handle, feel, and control items or equipment
- Reach with hands and arms
- Talk and hear
- See and be able to read, write, and interpret text
- Employee may use computer, phone, copier and other office equipment in the course of a day
- Occasionally lift and move objects weighing up to 10 pounds
- Employee may be required to travel for business purposes
- When working remotely, ability to secure confidential information
- When working remotely, perform all duties in a professional environment free of noise or anything that would create a negative customer experience
Work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential function of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Continuous is defined as 6\-8 hours, frequently is defined as 3\-6 hours, and occasionally is defined as up to 3 hours.
Work Schedule: Employee will be required to work a minimum of forty (40\) hours of per week or as many hours as it may take to perform above job duties. Schedule may vary based on business demands and will require a combination of office hours as well as work performed after hours and/or weekends at times.
Sarnova is an Equal Opportunity Employer. We offer a competitive salary, commensurate with experience, along with a comprehensive benefits package, including 401(k) Plan. EEO/M/F/Veterans/Disabled. Our mission is to be the best partner for those who save and improve patients’ lives. Excellence in delivering upon our mission is dependent upon having a diverse team that is empowered to bring their full, authentic self to work each day. We strive to create a workplace that reflects the communities we serve, and we are passionate
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sarnova HC, LLC, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sarnova HC, LLC AI Hiring
Sarnova HC, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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