Cloud Developer

Job Description:

In the HPE Chief Technology Office, 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 FCthem drive their business results. Join us redefine what’s next for you.

What you’ll do:

To strengthen and expand our AI/ML offerings, we are looking for a Machine Learning Engineer to design and develop solutions using AI tools and machine-learning models that have a big potential for value and scale at HPE

As a Machine Learning Engineer you build a wide spectrum of ML based systems and thrive working across technologies and codebases.

The ideal candidate should be involved in designing and develop machine learning algorithms and deep learning applications and systems. Also, should be able to execute tests and optimize machine learning models and algorithms.

• Experiment, design and develop solutions using AI tools and machine-learning models with a high potential for value and scale.
• Perform research and testing to develop or customize machine learning algorithms; conduct model training and evaluation as needed; integrate, test, tune and monitor the solutions developed.
• Work with Engineering Architects to develop the solutions you propose to ensure scalability and fit within the framework of the product/platform.
• Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
• Work independently, with limited day-to-day guidance from leadership.

What you need to bring:

• B.E/B.Tech/M.Tech/M.E degree in Computer Science or equivalent
• 5+ years of industry experience in applied Machine Learning, designing and developing scalable enterprise level solutions.
• Strong programming (Scala / Python / C++ or equivalent) and data engineering skills
• Deep understanding of Machine Learning best practices (eg. A/B test, feature engineering, feature/model selection, optimization), ML algorithms and Deep learning basics.
• Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Kafka (or equivalent).
• Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models.

Cloud Architectures, Cross Domain Knowledge, Design Thinking, Development Fundamentals, DevOps, Distributed Computing, Microservices Fluency, Full Stack Development, Release Management, Security-First Mindset, User Experience (X.