AI and ML Solution Architect

Hewlett Packard Enterprise • Spring, TX

4d • $141,000 - $325,000 • Hybrid

About The Position

The AI and ML Solution Architect develops and programs integrated software algorithms to structure, analyze, and leverage structured and unstructured data in product and systems applications. The role applies machine learning, statistical modeling, and deep learning techniques to improve product and system performance, data management, quality, and accuracy, and translates customer requirements and industry trends into AI/ML products, solutions, and system improvements.

The position researches emerging AI and machine learning technologies and designs scalable, efficient, and robust solutions with cross-functional teams. It develops, optimizes, deploys, and monitors machine learning models in production, integrates them with existing systems, and uses performance metrics to improve models and system performance. The role also provides technical leadership and mentorship, leads design reviews and team discussions, helps set design and implementation standards, and presents technical and business insights to senior stakeholders.

This is a hybrid role requiring an average of two days per week at an HPE office. The position is listed in Texas, United States, with an annual base salary range of USD 141,000–325,000. Variable incentives may also be offered.

Requirements

• Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline.
• Typically 7–10 years of experience.
• Deep understanding of machine learning algorithms, including linear regression, decision trees, support vector machines, random forests, neural networks, and reinforcement learning.
• Proficiency in model selection, hyperparameter tuning, and evaluating model performance with appropriate metrics.
• Strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, and statistical concepts.
• Programming proficiency in Python, R, or Java, including experience developing production-level code.
• Familiarity with software engineering best practices, version control systems such as Git, and software development methodologies.
• Advanced knowledge and experience in deep learning, including neural network architectures such as convolutional neural networks, recurrent neural networks, and transformers, as well as transfer learning, generative models, and deep learning optimization algorithms.
• Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques to complex problems.
• Excellent communication skills for collaborating effectively with cross-functional teams and stakeholders.
• Strong problem-solving and critical-thinking abilities to guide projects, make strategic decisions, and address complex technical challenges.
• Strong programming skills and experience with popular machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.

Nice To Haves

• Knowledge of libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.
• Deep understanding of statistical modeling, data mining, and data visualization.

Responsibilities

• Research and keep up to date with advancements in AI and machine learning technologies, frameworks, and algorithms; experiment with emerging techniques to solve complex problems and improve models.
• Collaborate with cross-functional teams to understand business requirements and design AI and machine learning solutions, selecting appropriate algorithms, models, and frameworks and architecting scalable, efficient, and robust systems.
• Develop, implement, and optimize machine learning models and algorithms, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and training on large datasets.
• Monitor and improve model performance and accuracy.
• Deploy machine learning models in production environments, accounting for scalability, performance, and security.
• Integrate models with existing software systems and infrastructure to ensure reliable operation and interoperability.
• Collect and analyze performance metrics for deployed models, identify areas for improvement, and fine-tune models and algorithms to enhance system performance.
• Organize and lead design review sessions, guide discussions toward project requirements and best practices, and mentor and provide feedback to junior and mid-level team members.
• Work with the engineering manager and team lead to establish design and implementation standards and support continuous improvement and alignment with project goals.
• Lead meetings and foster a collaborative, productive team environment.
• Provide technical leadership, mentorship, and guidance to junior team members.
• Address and resolve challenges proactively.
• Develop and deliver strategic presentations and reports to senior stakeholders, providing technical and business insights and recommendations.
• Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.

Benefits

• A comprehensive suite of benefits supporting physical, financial, and emotional wellbeing.
• Career development programs intended to help employees reach their career goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior