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About This Role
The AI Engineer designs and builds intelligent applications powered by Large Language Models (LLMs), AI agents, and enterprise knowledge systems. You'll work closely with engineers, manufacturing professionals, procurement teams, and business stakeholders to develop AI solutions that improve productivity, automate workflows, unlock insights from technical data, and solve real\-world business challenges. This is an opportunity to apply cutting\-edge AI technologies in a complex engineering and manufacturing environment where your work will have a direct impact on operations, decision\-making, and digital transformation initiatives. Location: Camden, NJ (On‑site) Primary Responsibilities:* Design, develop, and deploy Generative AI solutions using modern LLMs and enterprise AI platforms.
- Build AI\-powered copilots, assistants, and agents that help employees find information, automate tasks, and make better decisions.
- Design and implement Retrieval\-Augmented Generation (RAG) solutions leveraging engineering documents, technical specifications, manufacturing procedures, quality records, and enterprise knowledge repositories.
- Create AI workflows that integrate with business applications, databases, APIs, and enterprise systems.
- Develop and maintain Model Context Protocol (MCP) servers and integrations that allow AI assistants to securely interact with custom applications, enterprise systems, and business workflows.
- Build custom tools and agent\-based solutions that can retrieve data, perform actions, and automate business processes across multiple platforms.
- Deploy, monitor, and optimize production\-grade AI applications with a focus on reliability, scalability, security, and performance.
- Partner with engineering, manufacturing, procurement, quality, and IT teams to identify high\-value AI use cases and deliver practical solutions.
- Train, fine\-tune, evaluate, and optimize AI models to improve accuracy, user experience, and business outcomes.
- Establish evaluation, monitoring, and governance processes to continuously improve AI system quality and trustworthiness.
- Stay current with emerging AI technologies and identify opportunities to apply them across the organization.
Requirements:* Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- 3\-5 years of software engineering experience with at least 2\+ years focused on Generative AI, machine learning, or LLM\-based applications.
- Experience building and deploying enterprise AI, Generative AI, LLM, and RAG applications in production environments.
- Knowledge of modern AI engineering practices, including MLOps, CI/CD, model monitoring, evaluation, and lifecycle management.
- Strong understanding of AI security, governance, data privacy, and responsible AI principles.
- Experience with agent frameworks such as LangChain, Semantic Kernel, LangGraph, AutoGen, Microsoft Copilot Studio, CrewAI, or similar technologies.
- Experience integrating AI solutions with enterprise systems and business processes.
- Familiarity with document intelligence, knowledge management, computer vision, OCR, or multimodal AI solutions.
- Experience within engineering, manufacturing, energy, nuclear, aerospace, defense, or other regulated industries is preferred.
A Generation Ahead by Design™
*Investing in people who power the future.*
We believe great work deserves great rewards. Our benefits go beyond the basics, reflecting our commitment to supporting your whole self…your health, your future, and your career growth. This includes: Compensation:* Annual compensation based on skills and experience ranging from $120,000 to $140,000\.
Benefits:* Industry‑leading medical, dental, and vision coverage with a generous employer premium contribution and Day 1 eligibility!
- FLEX Program – Hybrid work opportunities available for eligible roles, where business needs and role requirements allow
- 401(k) retirement plan with up to a 5% company match and immediate vesting to support long‑term financial security
- Paid time off and 11 paid holidays to support rest, balance, and recharge
- Wellness program offering rewards for participation in company‑supported health initiatives
- Commuter benefits supporting mass transit and commuter parking options
- Company‑paid life and AD\&D insurance for added peace of mind
- Education assistance to support continued education and professional growth
- Employee support and voluntary benefits, including an Employee Assistance Program and optional coverage such as disability, legal, identity theft, and insurance programs
- Benefits eligibility and offerings may vary based on role, location, and employment status
If you’re driven to solve meaningful challenges and make a lasting impact, we invite you to build your career at Holtec—*a generation ahead by design™*.
Salary Context
This $120K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Holtec International, 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. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $120K to $140K.
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.
Holtec International AI Hiring
Holtec International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Camden, NJ, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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