Technology Program Analyst - AI

Key Skills

Aartificial intelligence, Agile methodologies, AI technology, Algorithms, Business Analytics, Business Development, Cloud computing, Data Analytics, Data science techniques, Design, Effective communication skills, Framework, Generative AI, High performance computing- HPC, KPIs, Large Language Models - LLMs, Leadership Skill, Machine learning techniques, Marketplace, Natural Language Processing (NLP), Neural Networks, NVIDIA Jetson, OpenAPI, Optimizely, Planning and organization, Product Development, Product Management, Project management, Recommendation Systems, Regulatory Compliance, Reinforcement Learning, SaaS, Software Development

Job Description

Description and Requirements

As a Global Technical Program Manager within the Lenovo Solutions & Services Group (SSG), specializing in AI Solutions, you will play a pivotal role in driving the conception, design, and implementation of new generative AI products, solutions for AI offerings. Having a blend of technical expertise, project management skills, and the ability to navigate the complexities of AI projects will be very critical , Your expertise will be essential in translating customer business requirements into innovative solution designs, expanding and enhancing the Lenovo Solutions for AI portfolio. Leading from design to delivery, you will spearhead managed services offerings based on generative AI capabilities and emerging innovations. The Global Technical Program Manager in AI plays a crucial role in driving the successful implementation of AI projects across geographical boundaries, requiring a combination of technical expertise, leadership skills, and effective communication.

Responsibilities:

• Collaboratively design, develop, and implement generative AI solutions across global teams, tailored to customer needs and requirements.
• Develop and implement project plans for AI initiatives, considering global objectives and regional requirements. Collaborate with key stakeholders to define project goals, scope, and deliverables.
• Develop a strategic roadmap for AI projects, considering the organization's long-term objectives. Monitor and report on project progress, identifying and addressing potential issues in a timely manner.
• Provide technical guidance and support to the project team, leveraging a strong understanding of AI concepts and methodologies & Stay informed about the latest trends and advancements in AI technologies.
• Work closely with cross-functional teams, including data scientists, engineers, product managers, and business analysts, to ensure alignment with project goals.
• Foster collaboration and communication among team members located in different geographical locations.
• Define and monitor key performance indicators (KPIs) to measure project success and identify areas for improvement.
• Communicate with clients, executives, and other stakeholders to provide updates on project status, address concerns, and gather feedback.
• Project Manage the Architecture of services leveraging and integrating frameworks such as NVIDIA Nemo, OpenAI, Avatar/Digital Person, and NLP technologies, ensuring optimal performance and compatibility.
• Project Manage the design, develop, and optimize global AI workload deployment and delivery across on-premise, hybrid, and multi-cloud environments, ensuring seamless operation and data integrity.
• Identify potential risks and develop mitigation strategies for AI projects & Monitor and manage risks throughout the project lifecycle to ensure successful outcomes.
• Program Manage the continuously enhancement and support architectures focusing on scalability, reliability, performance, cost-efficiency, and regulatory compliance.
• Champion security, compliance, responsible AI, and cloud best practices, fostering a culture of excellence and ethical conduct in technological endeavors.
• Collaborate with experts in deploying hybrid large language models (LLMs) and interactive AI capabilities.
• Keep updated and communicate rapidly evolving nature of AI technologies, a commitment to continuous learning and staying updated on industry trends is essential.

Qualifications:

• BE/MS in Computer Science, Software Engineering, or related fields.
• 7 or more years of experience
• 5+ years of experience Project or Program Managing the delivery of SaaS solutions in public cloud and on-premises environments.
• Strong understanding of Machine Learning fundamentals, including neural networks, reinforcement learning, and generative models.
• Exceptional technical leadership and communication skills.

Preferred Skills:

• Advanced knowledge in AI and ML frameworks basics and implementation skills
• Market experience in one or more industries: financial services, telecommunications, industrial manufacturing, energy, consumer products, high-tech, media & entertainment, and automotive.
• AI and Machine Learning Knowledge: A strong understanding of AI and machine learning concepts is crucial. This includes knowledge of various algorithms, frameworks, and methodologies.
• Data Science Understanding: Familiarity with data science principles and practices, including data preprocessing, feature engineering, and model evaluation.
• AI and Machine Learning Knowledge: A strong understanding of AI and machine learning concepts is crucial. This includes knowledge of various algorithms, frameworks, and methodologies.
• Data Science Understanding: Familiarity with data science principles and practices, including data preprocessing, feature engineering, and model evaluation.
• Understanding of the relationship between various open-source software and hardware components.
• Deep understanding of security best practices, data privacy laws, and compliance requirements in cloud and AI environments.
• AI and Machine Learning Knowledge: A strong understanding of AI and machine learning concepts is crucial. This includes knowledge of various algorithms, frameworks, and methodologies.
• Data Science Understanding: Familiarity with data science principles and practices, including data preprocessing, feature engineering, and model evaluation.
• Ability to articulate technical issues, resolutions, and recommendations for diverse stakeholders.