Job Description & How to Apply Below
This role has been designed as ''Onsite' with an expectation that you will primarily work 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
HPE Operations is our innovative IT services organization. It provides the expertise to advise, integrate, and accelerate our customers' outcomes from their digital transformation. Our teams collaborate to transform insight into innovation. In today's fast paced, hybrid IT world, being at business speed means overcoming IT complexity to match the speed of actions to the speed of opportunities. Deploy the right technology to respond quickly to market possibilities.
Join us and redefine what's next for you.
Responsibilities
What you will do:
Responsible for conducting advanced research in AI and machine learning. This includes staying up to date with the latest advancements in the field, exploring emerging technologies, and identifying opportunities to apply cutting-edge techniques to solve complex business problems.
Tasked with designing and architecting AI solutions for complex problems. This involves analyzing business requirements, understanding constraints, and proposing appropriate machine learning models and algorithms.
Responsible for considering scalability, performance, and maintainability while designing the solution.
Provides technical guidance and mentorship to junior team members. This includes sharing best practices, reviewing code and designs, and helping team members overcome technical challenges. Participate in technical discussions and provide thought leadership within the organization.
Works closely with stakeholders, such as product managers, data scientists, and business analysts, to understand their requirements and translate them into technical solutions. Collaborate with cross-functional teams to ensure alignment and successful AI and machine learning project implementation.
Responsible for driving continuous improvement and innovation in the organization's AI and machine learning practices. This involves identifying areas of improvement, exploring new techniques or technologies, and promoting the adoption of best practices.
Be involved in evaluating and integrating third-party tools or services that can enhance the capabilities of AI solutions.
Facilitates design review sessions for your projects, ensuring alignment with project requirements and best practices.
Mentor junior team members during review sessions.
Collaborates closely with the engineering manager and team lead to refine and iterating on design and implementation strategies, providing constructive feedback to peers.
Participates in and coordinates meetings, ensuring effective coordination and communication among team members.
Independently prepares and delivers detailed presentations and reports to stakeholders, translating complex technical concepts into understandable terms for non-technical audiences.
May be required to interpret and report data findings and maintain or update specific business intelligence tools, databases, dashboards, systems, or methods
May be involved in the design and development of solutions to complex application problems, system administration issues, or network concerns, where applicable to the role.
What you need to bring :
- Education And Experience Required
Bachelor's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline. Master's degree is desirable.
Typically, 4-7 years' experience.
Knowledge And Skills
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.
A strong foundation in mathematics and statistics. In-depth knowledge of linear algebra, calculus, probability theory, and statistical concepts. Understanding and developing complex machine learning models and algorithms.
Proficiency in programming languages such as Python, R, or Java is expected. Experience developing production-level code and familiarity with software engineering best practices, version control systems (e.g., Git), and software development methodologies are also required.…
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