Applied AI Engineer - Hybrid

NRG Energy, Inc.•Houston, TX

30d•Hybrid

About The Position

Welcome to the intersection of energy and home services. At NRG, we're all about propelling the next generation of leaders forward. We are driven by our passion to create a smarter, cleaner and more connected future. We deliver innovative solutions that make our customers' lives easier-helping them power, protect, and intelligently manage their homes and businesses. To do this, we need creative and talented people to join our company. We offer a dynamic work environment and a unified and inclusive culture. NRG fosters a strong sense of belonging that leads to better collaboration and business performance. Our company programs are designed to help employees develop the skills they need for success now and in the future. In everything we do, we aim to champion our employees and bring value to our customers, investors and society. More information is available at www.nrg.com. Connect with NRG on Facebook, Instagram, LinkedIn and X. Job Summary We are seeking a highly skilled Applied AI Engineer to join our Data & AI team as a full-time employee. This role is ideal for an engineer with deep expertise in Generative AI (GenAI) technologies and a proven track record of delivering production-grade AI solutions. The Applied AI Engineer will design, build, and scale GenAI applications, while also contributing to enterprise standards, reusable frameworks, and the overall maturity of our AI engineering practices. This position requires someone who can balance hands-on solution delivery with technical leadership in design, integration, and optimization, ensuring our AI initiatives deliver measurable business impact.

Requirements

Bachelor's or Master's degree in Computer Science, AI/ML, or related technical field.
5+ years of software development experience, with strong proficiency in Python.
3-5+ years hands-on experience building GenAI/LLM-based applications, with proven success from PoC to production deployment.
Proficiency in designing retrieval pipelines (document loaders, chunking strategies, embeddings, vector databases like FAISS, Pinecone, ChromaDB).
Expertise in LLM APIs (OpenAI, Claude, Gemini, etc.), prompt engineering, and fine-tuning.
Experience with cloud platforms (GCP, Azure), containerization (Docker, Kubernetes), and MLOps (CI/CD, monitoring).
Strong understanding of API design, microservices, and enterprise integration patterns.
Familiarity with version control systems (e.g., Git, Azure DevOps).
Demonstrated ability to build and scale AI solutions in production.

Nice To Haves

Experience with orchestration frameworks (LangGraph, LangChain, LlamaIndex).
Familiarity with DevOps practices such as IaC (Terraform), YAML pipelines, and automation.
Strong communication skills with ability to collaborate across teams and articulate technical concepts clearly.
Proactive, self-motivated problem solver with a track record of delivering high-value solutions.
Leverage vibe and agentic coding tools such as Cursor, Claude Code, and similar frameworks to accelerate AI solution development and orchestrate multi-agent workflows.

Responsibilities

Translate business requirements into robust, scalable AI solutions using RAG, embeddings, vector search, and fine-tuning.
Design, prototype, and implement LLM-driven applications with multi-step agent workflows and orchestration frameworks (e.g., LangGraph, LangChain, LlamaIndex).
Build and maintain APIs, services, and reusable components in Python to support AI applications.
Deploy and monitor AI models in cloud-native environments (GCP, Azure) leveraging Kubernetes, serverless, and MLOps pipelines.
Continuously evaluate model/system performance and implement improvements.
Contribute to the design of modular and reusable AI architectures across projects.
Establish and follow engineering best practices for GenAI development, testing, deployment, and monitoring.
Support the creation of documentation, templates, and playbooks for consistent solution delivery.
Partner with cross-functional teams to integrate AI capabilities into enterprise applications.
Work closely with business stakeholders to translate challenges into AI-powered solutions.
Share lessons learned and help drive adoption of AI practices across teams.
Ensure AI applications align with security, compliance, and responsible AI standards.