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- Tasks: Lead AI projects from concept to production, ensuring seamless deployment and performance optimisation.
- Company: Join Lenovo, a global tech leader with a bold vision for smarter technology for all.
- Benefits: Enjoy flexible work options, private medical care, discounts, and a positive work-life balance.
- Why this job: Be part of innovative AI solutions that impact businesses and communities worldwide.
- Qualifications: 5+ years in AI/ML deployment, degree in Computer Science or related field preferred.
- Other info: Willingness to travel up to 50% for client collaboration is required.
The predicted salary is between 43200 - 72000 £ per year.
This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board.
General Information
- Req #: WD00076655
- Career area: Sales Support
- Country/Region: United Kingdom
- State: Hampshire
- City: Farnborough
- Date: Wednesday, February 12, 2025
- Working time: Full-time
Why Work at Lenovo
We are Lenovo. We do what we say. We own what we do. We WOW our customers. Lenovo is a US$57 billion revenue global technology powerhouse, ranked #248 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world's largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. This transformation together with Lenovo's world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere.
Description and Requirements
You as AI Infrastructure engineer and project manager will install, configure, and deploy the tested and validated AI-based Proof of Concepts (PoCs) into full production environments for enterprise customers. You will focus on deploying the Nvidia AI Enterprise solutions on Lenovo hardware and ensuring seamless, scalable, and robust production implementations.
Your responsibilities:
- Lead end-to-end transitions of AI PoCs into production environments, managing the entire process from testing to final deployment.
- Configure, install, and validate AI systems using key platforms, including: VMware ESXi and vSphere for server virtualization, Linux (Ubuntu/RHEL) and Windows Server for operating system integration.
- Use Docker and Kubernetes for containerization and orchestration of AI workloads.
- Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production.
- Optimize deployed AI models for accuracy, performance, and scalability to ensure they meet production-level requirements and customer expectations.
- Serve as the primary technical lead for the AI POC deployment in enterprise environments, focusing on AI solutions powered by Nvidia GPUs.
- Lead technical optimizations aimed at resource efficiency, ensuring that models are deployed effectively within the customer's infrastructure.
- Ensure the readiness of customer environments to handle, maintain, and scale AI solutions post-deployment.
- Collaborate with stakeholders, including cross-functional teams (e.g., Lenovo AI BDMS, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule.
- Develop and deliver detailed documentation for each deployment, covering installation procedures, system configurations, and validation reports.
- Maintain ongoing, transparent communication with all relevant stakeholders, providing updates on project status and addressing any issues or changes in scope.
What do you need to succeed in this opportunity:
- Minimum 5+ years of experience in deploying AI/ML models using Nvidia GPUs in enterprise production environments.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience in AI infrastructure deployment is an advantage.
- Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale.
- Experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow.
- Good understanding of virtualization and containerization technologies to ensure robust and scalable deployments.
- Proficiency in deploying AI solutions across enterprise platforms, including VMware ESXi, Docker, Kubernetes, and Linux (Ubuntu/RHEL) and Windows Server environments.
What do we offer:
- Holiday purchase
- Private medical
- Income protection
- Attractive pension scheme
- Positive work life balance
- Learning and development
- Life insurance
- Lenovo and Motorola products discounts
- Lifestyle discounts
- Cycle to work
- MyGymDiscounts
- Mortgage advice and support
- Referral bonus
- Electric car salary sacrifice scheme
- Free onsite parking
This role requires the ability and willingness to travel up to 50% of the time to collaborate with clients on-site during PoC testing, system deployment, and production rollouts.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.
AI Infrastructure Engineer and Project Manager employer: Lenovo
Contact Detail:
Lenovo Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Infrastructure Engineer and Project Manager
✨Tip Number 1
Familiarise yourself with Nvidia AI Enterprise solutions and their deployment on Lenovo hardware. Understanding the specific technologies and tools used in this role will help you speak confidently about your experience during interviews.
✨Tip Number 2
Network with professionals in the AI infrastructure field, especially those who have experience with Nvidia GPUs and enterprise environments. Engaging with industry experts can provide insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your past projects involving AI/ML model deployments. Be ready to share specific examples of how you managed transitions from PoCs to production, highlighting any challenges you overcame.
✨Tip Number 4
Stay updated on the latest trends in AI infrastructure and containerisation technologies like Docker and Kubernetes. Showing that you are proactive about learning can set you apart from other candidates.
We think you need these skills to ace AI Infrastructure Engineer and Project Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI infrastructure and project management. Focus on your proficiency with Nvidia GPUs, containerization technologies like Docker and Kubernetes, and any successful projects you've led.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI technology and how your background aligns with Lenovo's mission. Mention specific projects where you transitioned AI PoCs to production and the impact of your work.
Showcase Technical Skills: Clearly outline your technical skills related to the job description. Include your experience with VMware ESXi, Linux (Ubuntu/RHEL), and AI/ML frameworks such as PyTorch and TensorFlow to demonstrate your fit for the role.
Highlight Collaboration Experience: Since the role involves working with cross-functional teams, emphasise your experience in collaborating with stakeholders. Provide examples of how you've successfully coordinated deployments and communicated project updates.
How to prepare for a job interview at Lenovo
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with Nvidia GPUs, AI/ML frameworks like PyTorch and TensorFlow, and your familiarity with deployment environments such as VMware ESXi and Docker. Highlight specific projects where you've successfully transitioned AI PoCs into production.
✨Demonstrate Project Management Skills
Since this role involves leading complex projects, be ready to share examples of how you've managed timelines, coordinated with cross-functional teams, and ensured successful project delivery. Discuss any challenges you faced and how you overcame them.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. For instance, you might be asked how you would handle a performance issue during an AI model deployment. Think through your approach to troubleshooting and optimising AI systems.
✨Communicate Clearly and Effectively
Effective communication is key in this role. Practice explaining complex technical concepts in simple terms, as you'll need to collaborate with various stakeholders. Be sure to convey your ideas clearly during the interview to demonstrate your ability to lead discussions.
