Innovation Engineer

Innovation Engineer

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.

Who We Are:

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. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description:

The Role:

We are seeking Senior AI/ML & Innovation Engineer who will be leading initiatives across Hybrid Cloud portfolio and thrives on a challenging and fast-paced environment.

Develops and programs integrated software algorithms to structure, analyse and leverage structured and unstructured data in product and systems applications. Can work with large scale computing frameworks, data analysis systems, and modelling environments.

Uses machine learning and statistical modelling techniques to improve product/system performance, data management, quality, and accuracy. Formulates descriptive, diagnostic, predictive and prescriptive insights/algorithms and translates technical specifications into code. Applies, optimizes and scales deep learning technologies and algorithms to give computers the capability to visualize, learn and respond to complex situations.

Documents procedures for installation and maintenance, completes programming, performs testing and debugging, defines and monitors performance metrics.

Contributes to the success of HPE by translating customer requirements and industry trends into AI/ML products, solutions, and systems improvement projects.

Applies advanced subject matter knowledge to solve complex business issues and is regarded as a subject matter expert. Provides expertise and partnership to functional and technical project teams and may participate in cross-functional initiatives. Exercises significant independent judgment to determine best method for achieving objectives. May provide team leadership and mentoring to others.

How you will make your mark:

Conducts research and stays up to date with the latest advancements in AI and machine learning technologies, frameworks, and algorithms. Explore and experiment with cutting-edge techniques to solve complex problems and improve existing models.

Collaborates with cross-functional teams to understand business requirements and design AI and machine learning solutions. Determine the appropriate algorithms, models, and frameworks to use and architect the overall system to ensure scalability, efficiency, and robustness.

Develops, implements, and optimizes machine learning models and algorithms. This includes data pre-processing, feature engineering, model selection, hyperparameter tuning, and training on large datasets. Continuously monitor and improve model performance and accuracy.

Deploys machine learning models into production environments, considering scalability, performance, and security considerations.

Experience in optimizing high-throughput, low-latency LLM inference pipelines. Leverages advanced serving engines such as vLLM or SGLang, and implements sophisticated memory optimization techniques—including KV cache management, paged attention, and continuous batching—to maximize compute efficiency and hardware utilization.

Designs and orchestrates autonomous agentic workflows and multi-agent systems (MAS), enabling seamless Agent-to-Agent (A2A) communication to automate complex, multi-step operations across.

Proven experience building and deploying AI agents utilizing advanced reasoning loops, tool calling, and agentic orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or Semantic Kernel).

Integrate models with existing software systems and infrastructure, ensuring smooth operation and interoperability.

Monitors the performance of deployed models, collects relevant metrics, and analyses data to identify areas for improvement. Based on insights gained from monitoring and analysis, fine-tune models, optimize algorithms, and enhance system performance.

Works collaboratively with the engineering manager and team lead to set design and implementation standards, ensuring continuous improvement and alignment with project goals.

Develops and delivers strategic presentations and reports to senior stakeholders, demonstrating a deep understanding of technical and business aspects. Provide insights and recommendations.

About you:

Bachelor's or master’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.

Typically, 4-7 years’ experience.

Deep understanding of machine learning algorithms, such as linear regression, decision trees, support vector machines, random forests, deep learning models (e.g., neural networks), and reinforcement learning. Proficient in model selection, hyperparameter tuning, and evaluating model performance using appropriate metrics.

Proficiency in programming languages such as Python, Go or Java is expected. Experience developing production-level code and familiarity with software engineering best practices, version control systems (e.g., Github), and software development methodologies are also required.

Strong understanding of GitHub CoPilot, Cursor, N8N, vibe coding, Windsurf, and similar technologies

Experience in Cloud Infrastructure (AWS, Azure, etc)

Knowledge of Open Source, Linux, etc

Understanding of Devops, SRE

Understanding advanced neural network architectures (e.g., convolutional neural networks, recurrent neural networks, transformers) and advanced techniques such as transfer learning, generative models, and optimization algorithms for deep learning.

Proven hands-on experience with LLM inference scaling and optimization. Deep understanding of memory-bound bottlenecks in model serving, KV cache optimization, and direct experience deploying large models using high-performance backends like vLLM or SGLang.

Actively staying updated with the latest AI and machine learning research advancements. Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques to solve complex problems.

Must have excellent communication skills to collaborate with cross-functional teams and stakeholders effectively. Possess strong problem-solving and critical thinking abilities to guide projects, make strategic decisions, and solve complex technical challenges.

Strong programming skills, with expertise in Python, Go, or Java, are necessary.

Experience with leading machine learning frameworks and libraries, including TensorFlow, PyTorch, and scikit-learn, is highly desirable.

Additional Skills:

Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset

Accessibility

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here .

Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.

What We Can Offer You:

Health & Wellbeing

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