AI Data Specialist

We are Lenovo. We do what we say. We own what we do. We WOW our customers.

Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 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. Lenovo's continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

Job Summary: As an AI Data Specialist, you will play a key role in the design, development, and deployment of an Agentic AI platform that streamlines the creation of AI-driven customer solutions. You will work closely with AI engineers and cross-functional teams to support the deployment, integration, and scaling of AI services across enterprise environments. In this role, you will leverage your expertise in data management, AI technologies, and development practices to build efficient, reusable, and scalable data-driven solutions. You will be responsible for ensuring that data pipelines, models, and AI components are effectively integrated into a cohesive and high-performing platform. To succeed, you should possess a strong understanding of AI and data ecosystems, demonstrate hands-on technical skills, and have the ability to transform complex data and AI tools into standardized, repeatable solutions that drive business value.

This is a remote role based in the US. Please see below for important information regarding the recruiting process for fully remote roles.

Job Responsibilities

AI Solution Development

• Design and deploy AI solutions using LLMs, SLMs, VLMs, and Agentic AI frameworks.

• Support AI architecture, solution design, and proof-of-concept initiatives.

• Build scalable data pipelines for ingestion, transformation, and enrichment.
• Develop RAG solutions using vector databases such as Milvus and PGVector.

• Integrate AI services, APIs, and microservices into enterprise platforms.
• Optimize model training and inference using GPU-accelerated technologies.
• MLOps & Operations
• Implement MLOps, LLMOps, and pipeline orchestration best practices.
• Monitor, optimize, and maintain AI workflows and platform performance.

• Ensure data security, governance, and compliance requirements are met.
• Create technical documentation and maintain high-quality, reusable code.

• Partner with cross-functional teams to deliver scalable AI solutions.
• Evaluate and adopt emerging AI and data technologies to improve platform capabilities.

• Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
• 5+ years of experience in AI/data engineering and implementation roles, including data engineering, machine learning, NLP, or Generative AI-focused data solutions.
• 2+ years of hands-on experience in building end-to-end Generative AI and data-driven solutions, including data preprocessing, embedding generation, and Retrieval-Augmented Generation (RAG) pipelines using LLMs and multimodal models (e.g., OpenAI, Anthropic, Llama, Hugging Face, Amazon Bedrock).

• Strong experience with data processing frameworks and pipeline development.
• Hands-on experience with vector databases such as Milvus, PostgreSQL (PGVector), Pinecone, or similar, including embedding management and semantic search implementations.
• Experience working with the NVIDIA AI ecosystem (e.g., GPUs, CUDA, TensorRT, NeMo) or equivalent acceleration technologies for efficient data processing, model training, and inference is a strong plus.
• Strong understanding of data architectures involving structured, semi-structured, and unstructured data, and building scalable pipelines to support AI/ML workloads.
• Experience integrating AI/data solutions across cloud and on-premise environments, including containerization and orchestration using Docker and Kubernetes.
• Proven experience implementing MLOps, LLMOps, and DataOps practices, including CI/CD pipelines, data versioning, monitoring, and lifecycle management using tools such as Jenkins, GitLab, MLflow, or similar.
• Strong programming skills in Python, with experience in data processing libraries (e.g., Pandas, PySpark), and familiarity with API development.
• Solid understanding of data governance, security, and compliance, including handling sensitive data and implementing access controls and encryption.
• Strong problem-solving skills and ability to translate complex data and AI requirements into scalable, reusable solutions.