AI/ML Engineer

Job description

AI/ML 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. ***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 Youll 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:*** - Bachelors 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. **Job:** Engineering **Job Level:** TCP\\_02 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