Principal Generative AI Engineer

About the job

In the HPE Hybrid Cloud, we lead the innovation agenda and technology roadmap for all of HPE. This includes managing the design, development, and product portfolio of our next-generation cloud platform, Green Lake. Working with customers, we help them reimagine their information technology needs to deliver a simple, consumable solution that helps them drive their business results. Join us redefine what’s next for you. Principal Generative AI Engineer As a Principal Generative AI Engineer, you will be responsible for driving the roadmap and building the next generation Generative AI Platform and GenAI Models/Algorithms that our userbase will leverage for years to come as we scale our efforts to implement GenAI in our Product. You should be comfortable with petabytes of data, writing crisp design documentation, and building, debugging, and maintaining highly available distributed systems. The GenAI platforms that you build will empower developers throughout HPE to build better AI products more quickly than ever before. You will be at the forefront of development, research, and implementation of innovative GenAI models, prompting strategies, agentic AI systems, and pipelines. The ideal candidate should have a strong background in applied generative AI approaches, large language models, multimodal AI, agentic AI, and production-level engineering. You should have excellent problem solving and communication skills, and be comfortable working with data scientists, product managers, and other engineers. You should have a passion for creating GenAI products and be able to think through the implications of the solutions you create. A successful candidate should have an advanced degree in computer science, mathematics, engineering, or a related field, and have a strong portfolio of GenAI/LLM projects. You should design and build highly scalable systems, generative AI algorithms, and deep learning applications. You should be able to execute tests and optimize GenAI models and prompt/agentic workflows. You should also be able to mentor and guide other GenAI Engineers in the team. What you will do: • Experiment, design, develop and maintain generative AI models (e.g., LLMs, multimodal models), agentic AI architectures, and pipelines with high potential for value and scale. • Collaborate with other GenAI engineers, data scientists, product managers, and other engineers to ensure successful implementation of generative AI solutions. • Perform research and testing to develop or customize GenAI algorithms, prompt engineering strategies, and agentic workflows; conduct model training, fine-tuning, and evaluation as needed; integrate, test, tune and monitor the solutions developed. • Research and evaluate new technologies and tools for generative AI, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic AI frameworks, model evaluation and safety tools. • Maintain and update existing generative AI systems, including prompt libraries, agent workflows, and deployed models. • Troubleshoot and debug GenAI systems, including issues related to model outputs, prompt reliability, hallucinations, and agentic behaviors. • Work collaboratively with cross-functional partners and stakeholders, identify opportunities for business impact, understand, refine, and prioritize requirements for GenAI models and solutions, drive engineering decisions, and quantify impact. • Hands-on development, productionization, and operation of GenAI models, agents, and pipelines at scale, including both batch and real-time use cases. • Work with large scale structured and unstructured data, build and continuously improve cutting edge GenAI models, including LLMs, vision-language models, and agentic systems. • Provide technical guidance and mentorship to other team members and interns in GenAI best practices, prompting strategies, and agentic design. • Identify areas of improvement in existing GenAI systems, including prompt engineering, agent reliability, and model deployment. What you will need: • B.E/B.Tech/M.Tech/M.E degree in Computer Science or equivalent • 10-12 years of industry experience in applied AI/GenAI, designing and developing scalable enterprise level solutions • Experience in architecting and building large, highly scalable systems & software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, agentic workflows, prompt orchestration) • Strong programming (Python / C++ or equivalent) and data engineering skills • Deep understanding of Generative AI best practices (e.g., prompt engineering, RAG, agentic design, model fine-tuning, optimization), LLMs, multimodal models, and deep learning basics. • Experience with these technologies: OpenAI GPT, Llama, Hugging Face Transformers, LangChain, DeepSpeed, Ray, Kubernetes, Spark, Kafka (or equivalent). • Industry experience building end-to-end GenAI infrastructure and/or building and productionizing Generative AI models, agents, and workflows • Experience with MLOps/LLMOps practices and tools (e.g., MLflow, Weights & Biases, DVC, SageMaker, Vertex AI) • Design, implement and integrate the next generation of Generative AI infrastructure to empower other Data Scientists and AI engineers to build GenAI models and agents that make real-time decisions. • You will collaborate with other engineers and data scientists to create optimal experiences on the Core GenAI platform, including but not limited to: prompt libraries, agentic orchestration, the real-time serving layer, and the offline training system • Strong collaboration and communication skills, both verbal and written • Bring a deep empathy for customer needs and insights as well as an intuitive grasp of the business problems we’re trying to solve Good to have: • Experience with traditional machine learning and deep learning frameworks and algorithms (e.g., RNNs, CNNs, Transformers, GANs) • Knowledge of reinforcement learning, transfer learning, and meta-learning concepts • Hands-on experience with TensorFlow, PyTorch, JAX, Keras • Familiarity with data labeling platforms, ML model monitoring and evaluation tools • Experience with MLOps/LLMOps practices and tools (e.g., MLflow, Weights & Biases, DVC, SageMaker, Vertex AI) • Exposure to model safety, bias detection, explainability, and responsible AI practices • Experience with cloud platforms (AWS, Azure, GCP) for scalable AI deployments • Contributions to open source GenAI/ML projects or research publications Additional Skills: Cloud Architectures, Cross Domain Knowledge, Design Thinking, Development Fundamentals, DevOps, Distributed Computing, Microservices Fluency, Full Stack Development, Release Management, Security-First Mindset, User Experience (UX)

Requirements

• Generative AI
• Large Language Models
• Python
• C++
• Data Engineering

Qualifications

• B.E/B.Tech/M.Tech/M.E degree in Computer Science
• Computer Science
• Engineering

Preferred Technologies

• Generative AI
• Large Language Models
• Python
• C++
• Data Engineering

About the company

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next.