AI/ML Engineer

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:

Job Family Definition:

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

Uses machine learning and statistical modeling 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.

Management Level Definition:

Contributions include applying intermediate level of subject matter expertise to solve common technical problems. Acts as an informed team member providing analysis of information and recommendations for appropriate action. Works independently within an established framework and with moderate supervision.

What You'll Do:

• Primary responsibility will be to design, develop, and implement machine learning models and algorithms. This involves researching, experimenting, and selecting appropriate models and techniques to solve specific business problems.

• Responsible for preparing and pre-processing large datasets for machine learning tasks. This includes data cleaning, normalization, feature extraction, and transformation to ensure the data is suitable for training and testing machine learning models.

• Will train machine learning models using appropriate algorithms and frameworks. This involves selecting and optimizing hyperparameters, cross-validating the models, and evaluating their performance using various metrics such as accuracy, precision, recall, and F1-score.

• Collaborate with cross-functional teams, including data scientists, software engineers, and stakeholders, to understand business requirements, gather feedback, and iterate on models and solutions. Effective communication and the ability to explain complex concepts to non-technical stakeholders are crucial in this role.

• Contribute to small sections of design review sessions, presenting your work and gathering feedback from the engineering manager or team leader.

• Deals with real-world datasets, understand data quality issues, and apply appropriate methods to prepare data for machine learning tasks.

• Provides feedback to peers during the design and implementation phases while actively seeking guidance from the engineering manager or team leader.

• Contribute to stand-up meetings by identifying potential issues early and proposing preliminary solutions.

• Prepare comprehensive presentations and reports, occasionally presenting them to stakeholders with supervision and guidance from the engineering manager or team leader, ensuring clarity and effectiveness in communication.

• May be required to interpret and report data findings and maintain or update specific business intelligence tools, databases, dashboards, systems, or methods.

What You Need to Bring:

• A solid understanding of mathematics, including linear algebra, calculus, and probability theory, is essential for working with machine learning algorithms. Additionally, a good grasp of statistical concepts and methodologies is necessary for model evaluation and analysis.

• Proficiency in programming languages such as Python, R, or Java is expected. Knowledge of relevant libraries and frameworks like TensorFlow, PyTorch, scikit-learn, or Keras is highly beneficial. Experience with SQL for data manipulation and database querying may also be necessary.

• Hands-on experience in developing and implementing machine learning models, including through internships, research projects, or previous job roles where you worked on machine learning initiatives.

• Practical experience with data cleaning, data pre-processing techniques, and feature engineering is important.

• Experience designing and developing machine learning models using algorithms such as linear regression, deciding trees, random forests, support vector machines, or deep learning models is crucial. Familiarity with model evaluation techniques, hyperparameter tuning, and cross-validation is also expected.

• Proficiency in software engineering principles and practices is valuable. Experience with version control systems (e.g., Git), software development methodologies, and deploying machine learning models in production environments is advantageous.

• Strong communication skills, both technical and non-technical, are important for collaborating with team members, explaining complex concepts, and presenting findings to stakeholders. The ability to work in cross-functional teams and adapt to evolving project requirements is highly valued.

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, 2-4 years’ experience.

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

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. 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.

Let's Stay Connected:

Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.

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HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.

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