PSEG AI Engineer interview questions & guide 2026

The following questions reflect the technical rigor and practical focus required for an AI Engineer at PSEG. Use these to identify patterns in how you should approach problem-solving, system design, and behavioral reflection.

Generative AI & NLP

• How would you design a RAG pipeline to minimize hallucinations when querying technical utility documentation?
• What are the trade-offs between different embedding models for domain-specific document search?
• How do you implement a multi-agent system to handle a multi-step customer service request?

Preparation for PSEG should be balanced between deep technical implementation and the ability to articulate your design choices. You are expected to demonstrate that you can build models that are not only performant but also maintainable and secure.

Technical Proficiency – You will be evaluated on your depth of knowledge in modern AI frameworks and your ability to write clean, efficient code. Be prepared to discuss the "why" behind your choice of libraries, frameworks, and architectural patterns.

System Design Thinking – Interviewers look for your ability to scale AI solutions. You should focus on the trade-offs between latency, cost, and accuracy, particularly in the context of LLM serving and vector databases.

Communication of Complexity – As an AI Engineer, you will often work with non-technical business partners. Your ability to translate complex AI metrics into business value is a key differentiator during the behavioral and design rounds.

The interview process at PSEG for engineering roles is designed to assess both your technical mastery and your alignment with the company’s operational standards. You can expect a structured journey that begins with a technical screen to validate your core competencies, followed by a series of deep-dive rounds focusing on design, coding, and behavioral fit.

The pace is deliberate, mirroring the company’s focus on reliability and accuracy. You will likely interact with multiple members of the AI and data engineering teams, providing you with a comprehensive view of the collaborative culture. Expect the process to be highly interactive, where interviewers encourage you to think aloud and iterate on your solutions in real-time.

The visual timeline outlines the progression from initial screening to deeper technical discussions. Use this to pace your study plan, ensuring you are comfortable with coding fundamentals early on and reserving the final stages for complex system design and behavioral scenarios.

Generative AI & LLM Implementation

This area evaluates your hands-on experience with modern generative models. Strong performance requires more than just calling APIs; it involves understanding the underlying architecture of RAG and the nuances of LLM evaluation.

Be ready to go over:

• RAG Pipeline Design – Strategies for chunking, retrieval, and re-ranking.
• Embeddings & Vector Search – Selecting vector databases and optimizing similarity search.
• LLM Evaluation – Metrics for hallucination detection and response quality.

ML System Design

You must demonstrate the ability to design end-to-end systems. This is where your understanding of system design for LLM serving becomes critical.

Be ready to go over:

• Scalability – Managing concurrent requests and inference latency.
• Monitoring – Detecting drift in production environments.
• Cost Management – Balancing model performance with infrastructure budget.

Coding & Problem Solving

This confirms your ability to write production-ready code. Focus on efficiency and readability.

Be ready to go over:

• Performance Tuning – Optimizing Python code for data-heavy tasks.
• Data Structures – Using the right tools for searching and filtering.

As an AI Engineer, your primary objective is to deploy AI solutions that drive operational efficiency at PSEG. You will be responsible for the full lifecycle of AI products, from initial data exploration and model prototyping to production deployment and monitoring.

You will work closely with data scientists to transition research models into scalable engineering solutions. A major component of your daily work will involve building and maintaining RAG pipelines, ensuring that the information retrieved by LLMs is accurate, timely, and secure. Additionally, you will be expected to contribute to the development of multi-agent systems that automate repetitive business tasks, allowing the organization to focus on higher-value decision-making.

A successful candidate for this role possesses a blend of rigorous engineering discipline and an experimental mindset.

• Must-have skills: Proficient in Python, experience with PyTorch or TensorFlow, familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate), and hands-on experience with LLM frameworks like LangChain or LlamaIndex.
• Experience level: A strong background in building and deploying machine learning models, preferably in a cloud environment (AWS, Azure, or GCP).
• Soft skills: Ability to thrive in a collaborative, cross-functional team and a clear communicator who can simplify technical challenges for stakeholders.
• Nice-to-have skills: Experience with MLOps tools, containerization (Docker/Kubernetes), and knowledge of cloud-native AI services.

Q: How difficult are the coding rounds? A: The coding rounds are calibrated for an experienced engineer, focusing on practical data manipulation and algorithm efficiency rather than obscure brain teasers.

Q: Does PSEG expect me to have deep knowledge of utility operations? A: While domain expertise in energy is a plus, it is not a requirement; the team values strong engineering fundamentals and an ability to learn the business context quickly.

Q: What is the team culture like? A: The culture is collaborative and focused on long-term reliability, reflecting the company’s identity as a critical infrastructure provider.

Q: How long does the hiring process typically take? A: The timeline can vary, but generally spans a few weeks from the initial screen to the final decision.

• Think aloud: When solving system design problems, explain your trade-offs clearly. Interviewers want to see your decision-making process.
• Focus on the "Why": Don't just list tools; explain why you chose a specific vector database or model architecture.
• Be ready for ambiguity: Real-world AI problems are often ill-defined. Show how you clarify requirements and break down problems.

The AI Engineer role at PSEG is a unique opportunity to apply advanced machine learning techniques to real-world infrastructure challenges. By mastering the nuances of RAG architectures, multi-agent systems, and LLM serving, you will be well-positioned to succeed in your interviews. We encourage you to continue your preparation by exploring additional interview insights, practice questions, and strategic resources on Dataford.

The provided salary range reflects the market compensation for this position, taking into account the level of technical expertise required. Candidates should interpret these figures as a starting point for negotiation, considering their specific years of experience and specialized skill sets in AI engineering.