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About This Role
Date: Oct 9, 2026
Location: Newark, NJ, US, 07101
Company: PSEG
Requisition: 84388
PSEG Company: PSEG Services Corp.
Salary Range: $ 121,200 - $ 199,200
Work Location Category: Hybrid
We’re one of the country’s largest energy companies, with a vision of powering a future where people use energy more efficiently and it’s safer and delivered more reliably than ever. We’re also deeply connected to the communities we serve, with more than 13,000 employees working together to support our customers and make a difference every day.
Here, you’ll have the stability and exciting opportunities that come with being a Fortune 500 company — along with a supportive, friendly work environment where your contributions are valued.
We offer a flexible work environment designed to balance employee needs with collaboration and operational excellence. Roles fall into two categories:
- Onsite: Employees work onsite daily.
- Hybrid: A blend of remote and onsite work, with at least three onsite days per week required.
As an employee, if you are regularly scheduled to work 20 or more hours per week, you will have access to a wide range of comprehensive benefits from day one, designed to support your total well-being: medical, dental, vision, parental leave and family leave programs, behavioral health programs, 401(k) with company match, life insurance, tuition reimbursement, and generous paid time off.
More than 13,000 people already call PSEG their work home, taking pride in providing safe, reliable service to millions of customers. If you’re looking for a place where you can build a meaningful career and help power and support our communities, we’d love to welcome you to the team.
PSEG is not offering visa sponsorship for this position.
Job Summary
The AI Solutions Engineer (Full Stack) is responsible for evaluating, prototyping, designing, developing, and delivering enterprise AI solutions across a rapidly evolving technology landscape. This role combines hands-on AI engineering, full-stack application development, solution architecture, cloud integration, and technology evaluation responsibilities to accelerate enterprise AI adoption and establish scalable, secure, and reusable AI solution patterns.
This role is expected to actively build and deliver AI-enabled applications and solutions. The position will partner closely with business stakeholders, enterprise technology teams, platform owners, cybersecurity, to rapidly prototype, implement, and operationalize AI capabilities across the enterprise.
Job Responsibilities
- AI Solution Development & Delivery: Lead and oversee the design, development, test, and deployment of enterprise AI solutions, AI agents, copilots, APIs, and intelligent workflows. Lead the build of AI-enabled applications, reusable services, and integrations using enterprise AI platforms and cloud technologies. Provide end-to-end solution delivery from proof of concept through production deployment. Collaborate with IT teams to operationalize, optimize, and support AI solutions.
- AI Architecture & Engineering: Design scalable, secure, and maintainable AI solution architectures aligned with enterprise standards. Develop reusable AI engineering patterns, integration frameworks, and best practices. Define integration approaches across enterprise platforms, cloud services, APIs, and data sources. Ensure AI solutions are scalable, observable, and operationally ready.
- Cross-Functional Collaboration: Partner with business stakeholders and AI Business Product Managers to evaluate AI opportunities and solution approaches. Collaborate with Enterprise Architecture, Cybersecurity, Infrastructure, and Platform teams throughout solution delivery. Participate in discovery workshops, technical assessments, architecture reviews, and solution design. Provide technical guidance to delivery teams and stakeholders.
- Responsible AI, Security & Operational Readiness: Ensure AI solutions comply with enterprise governance, cybersecurity, privacy, and Responsible AI standards. Incorporate security, monitoring, logging, and operational controls into AI solutions. Support governance reviews, risk assessments, deployment automation, and AI operational readiness. Contribute to emerging MLOps and lifecycle management practices.
- AI Prototyping & Technical Experimentation: Develop rapid proof-of-concepts to validate AI use cases and business value. Evaluate emerging AI technologies and enterprise platforms to determine the best-fit solution.
Job Specific Qualifications
Required
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field
- 8+ years of experience in software engineering, full-stack application development, cloud engineering, AI/ML technologies, or solution development
- Hands-on experience designing, developing, and integrating full-stack cloud-based applications, APIs, and enterprise solutions
- Experience prototyping, building, and delivering AI, Machine Learning, Generative AI, or intelligent automation solutions
- Experience developing web applications, APIs, workflow applications, or user-facing enterprise applications
- Strong understanding of cloud platforms, distributed systems, APIs, enterprise integration patterns, and modern application development practices
- Strong communication, problem-solving, and stakeholder engagement skills
- Compliance with the Department of Energy’s regulation 10 CFR 810 is required
Desired
- Experience with enterprise AI technologies and cloud AI services such as Azure OpenAI, AWS Bedrock, Azure AI Services, or similar platforms
Experience building AI agents, copilots, Retrieval-Augmented Generation (RAG) solutions, orchestration workflows, chat interfaces, or GenAI-enabled applications
- Familiarity with enterprise platforms including ServiceNow, Salesforce, SAP, and AI-enabled enterprise applications
- Experience with Python, REST APIs, orchestration frameworks, vector databases, and modern AI development frameworks
- Familiarity with front-end frameworks, lightweight UI development, cloud-native application development, and API integration
Some positions at PSEG require access to information covered by the Department of Energy’s regulation 10 CFR 810 (Part 810). If applicable, the successful applicant must prove they are: (1) a citizen or national of the USA; OR (2) a lawful permanent resident of the United States (Non-Conditional Permanent I-551 / Green Card / Permanent Resident Card holder); OR (3) a citizen, national, or permanent resident of a “Generally Authorized” destination on the attached list not also a citizen, national, permanent resident of any country not listed; OR (4) a “Protected Individual” under the Immigration and Naturalization Act (8 U.S.C 1324b(a)(3)).
As an employee of PSEG, you should be aware that during emergency restoration efforts, you may be required to perform functions outside of your routine duties and on a schedule that may be different from normal operations.
For all roles, PSEG’s drug and alcohol testing program includes pre-employment testing, testing for cause, and post-incident/accident testing.
Employees who are hired or transfer into a federally regulated role (including positions covered by USDOT, PHMSA, or NRC regulations) are subject to random drug and alcohol testing, inclusive of marijuana. Although numerous states throughout the country have legalized marijuana/cannabis products recreationally and medically, the use of these products are prohibited for employees in federally regulated roles. Please note that the use of CBD products may result in a positive drug test for THC/Marijuana and such use is not a legitimate medical explanation for a positive result.
If you are a current PSEG Long Island (PSEGLI) employee and offered an opportunity with PSEG or any of its subsidiaries other than PSEGLI, you will be treated as a new hire. Please note that as a new hire to PSEG, your benefits will change and generally will be consistent with other similarly situated PSEG new hires. Similarly, for PSEG employees who accept job opportunities with PSEGLI, your benefits will change and generally be consistent with other similarly situated new hires of PSEGLI.
PSEG is an equal opportunity employer, dedicated to a policy of non-discrimination in employment, including the hiring process, based on any legally protected characteristic. Legally protected characteristics include race, color, religion, national origin, sex, age, marital status, sexual orientation, disability or veteran status or any other characteristic protected by federal, state, or local law in locations where PSEG employs individuals.
PSEG is committed to providing reasonable accommodations to individuals with disabilities. If you have a disability and need assistance applying for a position, please call 973-430-3845 or email [email protected].
If you need to request a reasonable accommodation to perform the essential functions of the job, email [email protected]. Any information provided regarding a disability will be kept strictly confidential and will not be shared with anyone involved in making a hiring decision.
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Nearest Major Market: Newark
Nearest Secondary Market: New York City
Job Segment: Software Engineer, Engineer, Application Developer, Test Engineer, Cloud, Engineering, Technology
Salary Context
This $121K-$199K range is below the median 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 PSEG, 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 ($160K) sits 24% below the category median. Disclosed range: $121K to $199K.
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.
PSEG AI Hiring
PSEG has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Newark, NJ, US. Compensation range: $199K - $199K.
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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