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About This Role
Requisition Number: 106439
Sr AI Customer Engineer
Location:
This is a hybrid opportunity
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
Now is the time to bring your expertise to Insight. We are not just a tech company; we are a people-first company. We believe that by unlocking the power of people and technology, we can accelerate transformation and achieve extraordinary results. As a Fortune 500 Solutions Integrator with deep expertise in cloud, data, AI, cybersecurity, and intelligent edge, we guide organizations through complex digital decisions.
About the role
As an Sr Customer Engineer at Insight, you will work in a fast-paced, highly-skilled team developing state-of-the-art enterprise solutions using a wide variety of artificial intelligence technologies. You will work directly with customers, engineers, sales teams and partners to deliver high quality professional service engagements to Insight’s customers. Insight is a premier partner of Google Cloud Platform (GCP), and as such, your implementations will focus on enabling customers to best utilize GCP or to demonstrate its capabilities. You will be responsible for building cloud-native artificial intelligence solutions using Google’s Gemini Enterprise Agent Platform (GEAP) and all related technologies, such as Gemini foundation models, Agent Development Kit (ADK), Agent Studio, and Vector Search. You may also be responsible for implementing domain-specific solutions such as Gemini Enterprise for Customer Experience (GECX) or AI Commerce Search. This is a hands-on, senior position that will include both architecting and implementing enterprise AI solutions. Your approach to learning new technology wil be as important as any prior knowledge. We are looking for someone with a passion for continuous learning and a knack for problem-solving, who learns quickly and always says “I’ l figure it out.”
Along the way, you will get to:
- Implementing AI-based solutions for Insight clients across all project stages, from discovery and architecture, to development, testing and launch.
- Providing senior-level subject matter expertise and assistance to Insight clients and colleagues on artificial intelligence and agentic automation topics.
- Collaborating with client stakeholders managing the project, including other engineers and Director-level client resources.
- Creating technical documentation and leading solution design discussions.
- Assisting with technical pre-sales discussions and the review of statements of work. Your Working Style
- Systems-Minded Engineer: You care deeply about operational metrics like throughput, latency, and serialization boundaries, treating infrastructure stability as a critical component of model performance.
- Problem Solving: You possess excellent engineering instincts with a demonstrated ability to tackle complex, ambiguous data challenges across the entire machine learning and agent lifecycle.
- Strong Consultative Skills: You can translate complex AI and agentic concepts into actionable business strategies and solutions.
- Dynamic Communication: You are able to communicate complex technical concepts clearly to a broad range of internal and external stakeholders, from engineers to executive leadership. What we’re looking for
- 4+ years of relevant work experience in technical consulting, software development or cloud engineering.
- 1+ years of relevant work experience designing and implementing Generative AI solutions, including the use of large language models, model fine-tuning, vector embeddings, and AI application frameworks to build retrieval augmented generation (RAG) and multimodal workloads, preferably using the Gemini Enterprise Agent Platform on GCP.
- AI-Assisted Engineering: Hands-on experience leveraging modern AI code generation assistants and agentic IDE tooling, specifically GitHub Copilot, Claude Code, and Google Antigravity, to accelerate technical delivery, automate boilerplate development, and optimize the broader application execution loop.
- Proven experience implementing enterprise platform governance, security, and data boundaries using Agent Gateway, Agent Identity, and real-time compliance via Agent Guard / Model Armor.
- Hands-on experience implementing long-term context retention and multi-turn persistence in production environments using Google's Memory Bank architecture.
- Expertise in software development using Python, and familiarity with other programming languages such as JavaScript, Go or Java.
- Comfortable developing frontend web frameworks for AI prototyping, such as Streamlit, Gradio or React.
- Experience designing and implementing serverless and containerized solutions on common deployment runtimes, preferably Cloud Run, Google Kubernetes Engine, or Agent Runtime on Google Cloud Platform.
- Experience building and maintaining high-quality, developer-friendly server-side code, such as webhooks, cloud functions, and data pipelines.
- Experience with full stack development, including setting up cloud infrastructure, database services and application lifecycle management.
- Ability to architect, scope, and prioritize work based on feedback from the customer or internal teams.
- Experience working with analytical and transactional database systems, including SQL databases like MySQL, PostgreSQL or BigQuery.
- Experience participating in sales calls and working within a business or technical consulting environment. Useful Qualifications
- Exposure to containers and container orchestration, such as Docker.
- Experience working with interactive development environments like JupyterLab and integrated developer tools within the Google Cloud ecosystem.
- Familiarity with activating enterprise data directly where it lives using Knowledge Catalog & Smart Storage architectures to support zero-migration grounding.
- Experience stress-testing and optimizing agent execution paths before production deployment using Agent Simulation & Agent Observability suites.
- Exposure to model serving frameworks such as LLM and FastAPI and model training frameworks such as PyTorch and TensorFlow.
- Experience in writing automated tests, including unit, integration, and acceptance tests.
- Experience monitoring and scaling workloads based on latency or throughput demands.
The base salary range for this position is typically $130,000 to $150,000 with additional bonus and benefits available. However, compensation decisions are dependent on the facts and circumstances of each case, including experience and location, and we will also consider candidates outside of this range as necessary.
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.
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.
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.
Posting Notes: Chandler || Arizona (US-AZ) || United States (US) || Engineering || None || US - Chandler,AZ ||
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Insight AI Hiring
Insight has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Clarke City, Quebec, Canada, Chandler, AZ, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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