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About This Role
Why Turtle?
At Turtle, we’re redefining what it means to be an industry leader in electrical distribution, and we want you to be part of our exciting journey! For over 100 years, we’ve built a reputation for innovation, excellence, and exceptional customer service—and we’re just getting started. We are a diverse, passionate team spread across the US, Canada, Mexico, and Puerto Rico, committed to growth, learning, and creating opportunities for each other. At Turtle, every day is a chance to make a real difference, solve complex challenges, and push the boundaries of what’s possible. We believe in fostering an environment that inspires collaboration and sparks creativity, where employees are empowered to contribute to our mission and shape the future of the industry. If you're looking for a fast-paced, dynamic career where your ideas are valued and your growth is prioritized, Turtle is the place for you. Join us and be a part of a company that’s making waves and empowering its people to do extraordinary things every single day!
About the Role
The AI Operations Engineer is a hands-on, technical role responsible for building and supporting Turtle's day-to-day AI operations. The role develops and maintains AI agents and chatbots, migrates individually created or shadow AI tools into enterprise-managed and access-controlled equivalents, and keeps the underlying platforms, integrations, and cost structures healthy. The position works closely with the AI Center of Excellence, Data Services and Enterprise Architecture, and Cyber Security to deliver AI capabilities that are supportable, secure, and cost-effective.
This position is remote, with periodic travel to Turtle's Clark, NJ office for team meetings and stakeholder collaboration.
What You'll Do:
- Build, test, and support AI agents and chatbots that address internal business use cases, translating stakeholder requirements into working designs and improving them based on feedback and usage data.
- Maintain and continuously improve AI agents and chatbots in production, including prompt and workflow tuning, error handling, and performance monitoring.
- Identify individually created or informally deployed AI agents, bots, and chat tools across the organization and migrate them to enterprise-managed, centrally hosted, and access-controlled platforms.
- Apply consistent governance, authentication, and access control standards to migrated tools, in coordination with Cyber Security and the AI Center of Excellence.
- Administer and support core AI platforms, including NeuralSeek, Claude, and Copilot, and monitor platform health, performance, and usage using Data Dog and related observability tools.
- Evaluate new AI tooling and platform capabilities and recommend adoption where appropriate.
- Support and maintain Model Context Protocol (MCP) server infrastructure that connects AI agents to enterprise data and systems.
- Build, test, and maintain API integrations linking AI agents and chatbots to enterprise applications and data sources, and resolve integration issues with internal teams and vendors.
- Monitor and manage consumption-based AI costs, including token and API usage, and support cost allocation, budgeting, forecasting, and optimization without compromising performance.
- Maintain an up-to-date inventory of AI agents, bots, and integrations in production, including ownership, data access, and business purpose.
- Participate in incident response for AI-related issues, including outages, integration failures, and unexpected model behavior.
- Support vendor management for AI platform providers, including renewals, license reconciliation, and issue escalation.
- Develop and deliver training and enablement materials so business users adopt enterprise AI tools instead of building their own.
- Ensure agents and integrations comply with Turtle's AI governance, data handling, and security policies.
- Document architecture, configurations, and runbooks for supported agents, chatbots, and integrations to reduce key-person dependency.
What You'll Bring
Education:
- Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent hands-on experience.
Experience:
- 3 to 5 years of experience in an AI, automation, integration, or DevOps/AIOps engineering role.
- Practical experience building or supporting AI agents or chatbots in a business environment.
Skills:
- Working knowledge of REST APIs and experience building or maintaining API integrations.
- Strong troubleshooting skills across applications, integrations, and infrastructure.
- Ability to work independently while collaborating across IT, business, and security teams.
Preferred:
- Familiarity with NeuralSeek, Claude, Microsoft Copilot, or comparable AI orchestration and assistant platforms.
- Experience with Data Dog or similar observability and monitoring platforms.
- Exposure to Model Context Protocol (MCP) or similar agent-to-system integration frameworks.
- Understanding of cloud or SaaS cost management (FinOps) principles as applied to AI consumption.
- Experience operating in a regulated or compliance-driven environment (for example, ISO 27001 or NIST CMMC).
- Relevant certifications (for example, in AI platforms, cloud services, or FinOps) are beneficial but not required.
What We Offer
We offer a competitive benefits package that includes:
- 401(k) plan
- Health insurance
- Dental insurance
- Vision insurance
- Life insurance
- Paid holidays
- Vacation
- Employee negotiated discounts
Who We Are
Founded in 1923, Turtle is a fourth-generation, family-owned, and three-generation women-owned business, and one of the nation’s largest independent electrical and industrial distributors. Headquartered in Clark, NJ, our Electrical Distribution division operates 14 branches spanning from coast to coast. It is a significant force in the engineering and procurement of power distribution, automation, lighting, and energy projects, offering integrated services for the industrial and construction markets. Turtle Integrated provides on-site MRO procurement, cost-saving, and spend analytics across the US and in Canada, Puerto Rico, and Mexico.
What To Do Next
You can begin by filling out our application online. If you want to learn more about Turtle, please visit our website www.turtle.com or our LinkedIn: @Turtle.
Turtle is proud to be is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, gender identity or expression, national origin, disability, genetic information, pregnancy, marital status, military or veteran status, or any other protected characteristic as outlined by federal, state, or local laws.
Salary Context
This $100K-$125K 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 Turtle, 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 ($112K) sits 47% below the category median. Disclosed range: $100K to $125K.
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.
Turtle AI Hiring
Turtle has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Clark, NJ, US. Compensation range: $125K - $125K.
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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