Sr. AI Developer

Key Responsibilities

• Lead and manage a small team of AI developers to build AI platforms, implement agents, and customize standard solutions.
• Develop Agentic AI platform solutions, ensuring alignment with Lenovo's strategic goals and enterprise technical standards.
• Create AI architectures, inference engines, guardrails, and user interfaces for enterprise AI roll out.
• Develop multi-agents/super agents using large language models (LLMs), small language models (SLMs), vision language models (VLMs), leverage agent frameworks and standard protocols such as MCP.
• Integrate AI agents into existing platforms/systems, including on-premise and on the edge.
• Identify and implement AI tools, frameworks, and technologies that enhance the effectiveness and efficiency of AI solutions.
• Incorporate robust security measures into AI solutions, including data encryption, access controls, and vulnerability assessments.
• Implement best practices for agent evaluation on both experimental and production environments.
• Adhere to coding guidelines and best practices across projects, ensuring high-quality and maintainable AI software development.
• Communicate technical concepts and solution approaches clearly to both technical and non-technical stakeholders.
• Stay current with advancements in AI technologies and propose innovative implementation strategies.

Basic Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
• Proven track record of managing a small AI team and acting as the technical lead to complete enterprise AI platforms and/or AI solutions in production environments.
• 5+ years in AI development and implementation roles across domains such as data science, machine learning, NLP, computer vision, or Generative AI.

Preferred Qualifications

• 2+ years of experience in developing Generative AI end-to-end solutions, including multi-modal solutions utilizing NVIDIA, cloud-based LLMs, or open-source models (e.g., OpenAI, Anthropic, Llama, Hugging Face, Bedrock).
• Experience with NVIDIA technologies (e.g., GPUs, CUDA, TensorRT) and integrating tools from NVIDIA AI Enterprise in on-premise solutions.
• Strong knowledge of on-premise and cloud environments, particularly with containerization and scaling using Kubernetes.
• Proven experience in implementing MLOps, LLMOps, or AIOps, as part of enterprise platforms and managing CI/CD pipelines for AI deployment using tools such as Jenkins, GitLab, or similar.
• Strong programming skills in languages such as Python, React, and scripting languages.