AI and Machine Learning Engineering Graduate

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:

Contributes to assignments of limited scope by applying technical concepts and theoretical knowledge acquired through specialized training, education, or previous experience. Acts as team member by providing information, analysis and recommendations in support of team efforts. Exercises independent judgment within defined parameters., * Designs, develops and implements AI and machine learning models. This includes data pre-processing, feature engineering, algorithm selection, model training, and evaluation.

• Gathers and analyzes relevant data sets to train and test machine learning (ML) models. Understands data analysis techniques, statistical methods, and data visualization to gain insights from the data and make informed decisions.
• Optimizing algorithms and models to improve their performance is an essential responsibility for AI and Machine Learning Engineers. This involves fine-tuning hyperparameters, conducting experiments, implementing optimization techniques, and employing feature selection or dimensionality reduction methods.
• Works closely with cross-functional teams, including data scientists, software engineers, product managers, and domain experts. Collaboration is crucial for understanding project requirements, aligning objectives, and integrating AI/ML solutions into existing systems or products.
• Keeping accurate and up-to-date AI and machine learning project documentation. Document work, including methodologies, code, and experimental results. Responsible for preparing reports and presenting findings or recommendations to stakeholders.
• Participates in regular design review sessions with the engineering manager, team leader, and other stakeholders to ensure that design choices align with project requirements and best practices.
• Actively seeks and incorporates feedback from the engineering manager or team leader during the design and implementation phases to ensure continuous improvement and adherence to project goals.
• Attends daily or weekly stand-up meetings led by the engineering manager or team lead providing updates on progress, discuss challenges, and coordinate efforts with other team members.
• Prepares and delivers detailed presentations and reports to stakeholders as directed by the engineering manager and team lead, ensuring that complex technical information is communicated clearly and effectively.
• May be required to interpret and report data findings and maintain or update specific business intelligence tools, databases, dashboards, systems, or methods.

• Bachelor’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline. Master’s degree is desirable.
• Typically, 0-2 years’ experience.

Knowledge and Skills:

• Proficiency in programming languages such as Python, R, or Java is required. Knowledge of libraries and frameworks commonly used in AI and machine learning, such as TensorFlow, PyTorch, or scikit-learn, is highly beneficial.
• A solid understanding of statistics, probability, linear algebra, calculus, and optimization methods is crucial for building and evaluating machine learning models.
• In-depth knowledge of machine learning algorithms, techniques, and concepts is essential. This includes supervised and unsupervised learning, deep learning, neural networks, reinforcement learning, and natural language processing.
• Proficiency in working with large datasets, data pre-processing, data cleaning, and exploratory data analysis is necessary. Experience with SQL and databases and data visualization tools like Matplotlib or Tableau is required.

“The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level.

• United States of America: Annual Salary USD 81,700 - 187,800 in North Carolina The listed salary range reflects base salary. Variable incentives may also be offered.”

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.