Senior AI and Machine Learning Engineer

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 impact technical components of HPE products, solutions, or services regularly and sustainable. Applies advanced subject matter knowledge to solve complex business issues and is regarded as a subject matter expert. Provides expertise and partnership to functional and technical project teams and may participate in cross-functional initiatives. Exercises significant independent judgment to determine best method for achieving objectives. May provide team leadership and mentoring to others.

What you’ll do:

• 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. Additionally, knowledge of libraries and frameworks like TensorFlow, PyTorch, sci-kit, and Keras is a plus.
• Advanced knowledge and experience in deep learning. Understanding advanced neural network architectures (e.g., convolutional neural networks, recurrent neural networks, transformers) and advanced techniques such as transfer learning, generative models, and optimization algorithms for deep learning.
• Actively staying updated with the latest AI and machine learning research advancements. Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques to solve complex problems.
• Must have excellent communication skills to collaborate with cross-functional teams and stakeholders effectively. Possess strong problem-solving and critical thinking abilities to guide projects, make strategic decisions, and solve complex technical challenges.
• Strong programming skills, with expertise in Python, R, or Java, are necessary. Experience with popular machine learning frameworks and libraries like TensorFlow, PyTorch, or sci-kit is essential.
• A deep understanding of statistical modeling, data mining, and data visualization is highly preferred.

• Conducts research and stays up to date with the latest advancements in AI and machine learning technologies, frameworks, and algorithms. Explore and experiment with cutting-edge techniques to solve complex problems and improve existing models.
• Collaborates with cross-functional teams to understand business requirements and design AI and machine learning solutions. Determine the appropriate algorithms, models, and frameworks to use and architect the overall system to ensure scalability, efficiency, and robustness.
• Develops, implements, and optimizes machine learning models and algorithms. This includes data pre-processing, feature engineering, model selection, hyperparameter tuning, and training on large datasets. Continuously monitor and improve model performance and accuracy.
• Deploys machine learning models into production environments, considering scalability, performance, and security considerations.
• Integrate models with existing software systems and infrastructure, ensuring smooth operation and interoperability.
• Monitors the performance of deployed models, collects relevant metrics, and analyzes data to identify areas for improvement. Based on insights gained from monitoring and analysis, fine-tune models, optimize algorithms, and enhance system performance.
• Organizes and leads comprehensive design review sessions, driving discussions to align with project requirements and best practices. Mentor and provide feedback to junior and mid-level team members.
• Works collaboratively with the engineering manager and team lead to set design and implementation standards, ensuring continuous improvement and alignment with project goals.
• Regularly leads meetings, fostering a collaborative and productive team environment.
• Has experience in providing technical leadership, mentorship, and guidance to junior team members.
• Address and resolve challenges proactively.
• Develops and delivers strategic presentations and reports to senior stakeholders, demonstrating a deep understanding of technical and business aspects. Provide insights and recommendations.
• Applies and leverages data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.

What you need to bring:

• Bachelor's or master’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
• Typically, 7-10 years’ experience.