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About This Role
Requisition Number: 105933
Principal AI Engineer
Focus
Agentic Systems, Google Cloud \& Vertex AI, LLMs, and Clinical Operations Engineering
Location
Nashville, TN area preferred
Insight at a Glance
- 14,000\+ engaged teammates globally
- $8\.2 billion in revenue in 2025
- Certified as a Great Place to work in 9 Countries in 2025
- Fortune 500 Company (No. 447\) in 2025
- Received 25\+ industry and partner awards in the past year
- $1\.4M\+ total charitable contributions in 2024 by Insight globally
About the Role
Now is the time to bring your expertise to Insight. Healthcare and enterprise organizations are rapidly adopting large language models, generative AI, and agentic systems, but many face a critical challenge: moving beyond demos and prototypes into secure, maintainable, production\-grade AI applications that integrate with real clinical and operational workflows.
We are seeking a Principal AI Engineer with deep experience in agentic systems, Google Cloud Platform and Vertex AI, large language models, clinical operations, and forward deployed engineering. In this client\-facing consulting role, you will design and build AI\-enabled applications that connect clinical and enterprise data, tools, workflows, and users through scalable, governed engineering patterns.
You will bridge the gap between AI strategy and production implementation, partnering with clinicians, architects, data teams, security leaders, and operations stakeholders to deliver solutions that are useful, observable, secure, and ready for enterprise adoption.
What You'll Do
- Agentic AI Solution Engineering: Design and build agentic AI systems that reason across tasks, use tools, retrieve context, and orchestrate multi\-step workflows to automate and optimize clinical and operational processes, with human\-in\-the\-loop review.
- Google Cloud \& Vertex AI Delivery: Develop AI solutions using Google Cloud technologies such as Vertex AI, Gemini models, Vertex AI Agent Builder, Vertex AI Search, Document AI, BigQuery, and related Google Cloud services.
- Clinical Note \& Document Processing: Build LLM\-powered pipelines to extract, summarize, and structure clinical notes and unstructured healthcare documents, improving accuracy, speed, and downstream operational workflows.
- MLOps and AI Delivery Automation: Establish CI/CD and MLOps pipelines, infrastructure\-as\-code, environment management, automated testing, release controls, and observability practices for AI\-enabled applications on Google Cloud.
- RAG and Enterprise Knowledge Systems: Build retrieval\-augmented generation solutions that connect securely to clinical content, structured data, EHR and document repositories, and operational systems.
- Security, Identity, and Governance: Implement authentication, authorization, RBAC, data access controls, logging, auditability, and guardrails to ensure AI systems handle PHI safely and operate compliantly in regulated healthcare environments.
- Evaluation and Quality Engineering: Define and implement testing and evaluation approaches for agent performance, prompt quality, retrieval relevance, hallucination risk, response quality, latency, and reliability.
- Forward Deployed Technical Leadership: Serve as a hands\-on, forward deployed senior engineer and technical advisor, embedding with client teams to make architecture decisions, resolve implementation blockers, and move AI solutions from prototype to production.
- Practice Enablement: Mentor engineers and consultants while contributing reusable agentic design patterns, reference architectures, DevOps templates, and Google Cloud AI delivery accelerators for Insight.
What We're Looking For
- Experience: 6\+ years of experience in software engineering, cloud engineering, AI engineering, enterprise application development, or solution architecture, ideally in consulting, healthcare, or client\-facing delivery environments.
- Agentic Systems Expertise: Hands\-on experience designing and building agentic AI applications, including orchestration, tools, memory, planning, multi\-step workflows, RAG, and human\-in\-the\-loop controls.
- Google Cloud AI Platform Depth: Strong experience with Vertex AI and the broader Google Cloud AI ecosystem, including Gemini models, Vertex AI Agent Builder, Vertex AI Search, Document AI, BigQuery, or related Google Cloud services.
- Software Engineering Foundation: Strong proficiency in modern programming languages and frameworks commonly used for AI application development, such as Python, TypeScript, Go, FastAPI, LangChain/LangGraph, or similar technologies.
- DevOps and Cloud Engineering: Experience with GitHub Actions, Cloud Build, CI/CD, infrastructure\-as\-code (Terraform), containers (GKE and Cloud Run), APIs, monitoring, logging, environment promotion, and production release management.
- Clinical Operations \& Healthcare Data: Experience integrating AI with clinical and operational systems—EHRs, clinical documentation, and healthcare data standards such as HL7 and FHIR—including handling of PHI in regulated environments.
- AI Quality and Observability: Understanding of AI evaluation, prompt/version management, automated testing, telemetry, tracing, monitoring, model behavior analysis, and operational support patterns.
- Consulting Mindset: Strong communication skills with the ability to translate technical tradeoffs into practical recommendations for executives, clinical leaders, platform teams, security stakeholders, and operations users.
Preferred Certifications
- Google Cloud / AI: Google Cloud Professional Machine Learning Engineer, Professional Cloud Architect, Generative AI Leader, or relevant Google Cloud and AI certifications.
- DevOps / Engineering: GitHub, Google Cloud Professional DevOps Engineer, Kubernetes (CKA), Terraform, or cloud\-native engineering certifications.
- Healthcare / Governance: HIPAA, Responsible AI, or healthcare data and AI governance\-related certifications are a plus.
What you can expect
We’re legendary for taking care of you, your family and to help you engage with your local community.
But what really sets us apart are our core values of Hunger, Heart, and Harmony, which guide everything we do, from building relationships with teammates, partners, and clients to making a positive impact in our communities.
Join us today, your ambITious journey starts here.
*Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.*
*When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.*
*At Insight, we celebrate diversity of skills and experience so even if you don’t feel like your skills are a perfect match \- we still want to hear from you!*
*Insight does not accept unsolicited resumes from recruiters or employment agencies. Unsolicited resumes will be treated as direct applications from the candidate, and recruiters or agencies who submit candidates for this position without a prior, written vendor agreement will not be eligible for any form of compensation, even if the candidate is hired.*
The position described above provides a summary of some the job duties required and what it would be like to work at Insight. For a comprehensive list of physical demands and work environment for this position, click here.
Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.
Posting Notes: Nashville \|\| Tennessee (US\-TN) \|\| United States (US) \|\| Data \& AI \|\| None \|\| US \- Nashville, TN \|\|
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 Insight, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Insight AI Hiring
Insight has 4 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Phoenix, AZ, US, Nashville, TN, US, TN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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