AI and ML Solution Architect

AI and ML Solution Architect

This role has been designed as 'Onsite with an expectation that you will primarily work from an HPE office.

Job Description:

Analyzes, designs, 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.

Contributions have visible technical impact on a product or major subcomponent. Applies in-depth professional knowledge and innovative ideas to solve complex problems. Visible contributions improve time-to-market, achieve cost reductions, or satisfy current and future unmet customer needs. Recognized internal authority on key technology area applying innovative principles and ideas. Provides technical leadership for significant project/program work. Leads or participates in cross-functional initiatives and contributes to mentorship and knowledge sharing across the organization.

• Develops organization-wide architectures and methodologies for AI applications design and development across multiple platforms and organizations within the Global Business Unit.
• Responsible for designing, developing, and deploying advanced machine learning models and algorithms. This includes selecting appropriate techniques, data pre-processing, feature engineering, model training, and evaluation.
• Stays up to date with the latest advancements in the field and leads research initiatives to explore novel approaches and technologies. This involves conducting experiments, evaluating new algorithms, and identifying opportunities for innovation.
• Responsible for designing the architecture of AI systems and ensuring scalability, performance, and reliability. This includes optimizing algorithms, leveraging distributed computing frameworks, and utilizing cloud services to enable efficient and effective AI solutions.
• Works closely with other teams, such as data scientists, software engineers, and product managers. You will collaborate to understand requirements, identify opportunities for AI integration, and provide technical guidance throughout the development process.
• Provides technical leadership and mentorship to junior engineers, guiding them in best practices for AI and machine learning. Review their work and provide feedback to help them grow and improve their skills.
• Oversees and guides multiple design review sessions across different projects, ensuring consistency in design choices and adherence to best practices. Act as a key mentor for the team.
• Partners with the engineering manager and team lead to establish long-term design and implementation strategies.
• Leads efforts to incorporate feedback loops and continuous improvement processes.
• Leads meetings, ensuring efficient progress tracking, issue resolution, and team coordination. Support the engineering manager in setting meeting agendas.
• Creates and delivers high-level presentations and reports to executive stakeholders, effectively communicating complex technical strategies and their impact on business goals.
• Applies and leverages data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets
• May be involved in the design and development of solutions to complex applications problems, system administration issues, or network concerns, where applicable to the role.

• Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
• Typically, 10-15 years experience.

• Solid understanding of fundamental AI and machine learning concepts, including supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision, and statistical modeling.
• Proficient in implementing and deploying various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks. Knowledge of popular machine learning frameworks and libraries like TensorFlow, PyTorch, or sci-kit is required.
• Expertise in deep learning techniques, architectures, and frameworks (e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), etc.) is highly valuable.
• Strong programming skills are necessary for implementing and deploying machine learning models. Proficiency in Python, Java, or C++ is required. Ability to write clean, efficient, and maintainable code and have knowledge of software engineering best practices.
• Be skilled in preparing and cleaning data for machine learning tasks, which include data wrangling, feature extraction, dimensionality reduction, handling missing values, and addressing data quality issues.
• A solid understanding of mathematical concepts, such as linear algebra, calculus, probability theory, and statistics, is crucial for effectively designing and evaluating machine learning models.
• Proficiency in data visualization tools and techniques to analyze and present insights gained from AI and machine learning models. Create meaningful visualizations and effectively communicate complex concepts to technical and non-technical stakeholders.
• Familiarity with software engineering principles, version control systems (e.g., Git), testing methodologies, and agile development practices is valuable. This helps ensure the robustness, scalability, and maintainability of AI systems.
• Stay up to date with the latest advancements in the field, follow research papers, attend conferences, and contribute to the AI community. A passion for continuous learning and a drive for innovation are essential traits.
• Experience in leading research initiatives, publishing research papers, or contributing to open-source projects in AI.
• Experience in guiding and mentoring other engineers.
• Strong communication skills are necessary to collaborate with cross-functional teams effectively, explain complex AI concepts to non-technical stakeholders, and provide technical leadership and mentorship to junior engineers.

Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset

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.

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#greenlakecloudplatform, #hybridcloud

Job:

Engineering

Job Level:

TCP_05

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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