Gen-AI Engineer

The Cisco IT team is changing the way we run Cisco's operations by leveraging the power of technology, the best of business processes, and utilizing outstanding data insights. We are redefining how Cisco designs and delivers the employee, partner, and customer experience based on a culture that values customer service. We strive for speed and agility in all that we do. Above all else, we are kind to each other. We aspire to be an industry-leading IT team, with a strong focus on AI and security to differentiate us and foster innovation. To achieve simplicity and the best employee and customer experience, we need excellent talent and the right abilities to succeed. Who You'll Work With You will work with our amazing Data Infrastructure & Platforms team as part of the Infrastructure & Cloud Services. You will build solutions and support them across our portfolio of capabilities, primarily focusing on enabling AI/ML and Generative AI capabilities in a multi-functional team setup. You will collaborate with other engineers, architects, and organization leadership. Who You Are We are looking for a highly skilled Senior GenAI Engineer to lead the deployment and management of on-premise Large Language Models (LLMs), with a focus on Retrieval Augmented Generation (RAG). This role requires expertise in developing and supporting large-scale GenAI and ML platforms, with a strong emphasis on document management, security, vector databases, and workflow orchestration. The successful candidate will have extensive experience in responsible AI practices, including toxicity screening and ensuring regulatory compliance across AI solutions. What You’ll Do Deploy and manage Large Language Models (LLMs) for on-prem environments, focusing on Retrieval Augmented Generation (RAG) and ensuring high-performance infrastructure. Build and optimize secure, scalable AI/ML platforms that support enterprise-level document management and data security protocols. Design, implement, and manage workflows to ensure seamless document processing, ingestion, classification, and retrieval within AI models. Implement and manage vector databases to support efficient document search and retrieval within AI workflows. Ensure compliance with organizational and regulatory data security standards, including encryption, access control, and auditing of sensitive documents used within AI models. Implement and maintain responsible AI practices, including toxicity screening, bias detection, and regulatory compliance to ensure ethical and safe AI usage. Collaborate with cross-functional teams to ensure data privacy and information security requirements are met when processing documents through AI models. Continuously evaluate and improve infrastructure to support evolving AI/ML needs, particularly focusing on document ingestion, classification, and retrieval. Develop and maintain automated pipelines for LLM deployment and secure document processing with real-time monitoring and alerts. Work closely with compliance, legal, and governance teams to ensure AI models are aligned with security and regulatory frameworks. Stay updated on the latest advancements in AI/ML, document security, and responsible AI practices. Minimum Qualifications Bachelor's in Computer Science, Computer Engineering, Electrical Engineering, or a related STEM field. 7+ years of experience in engineering, with at least 2 years specifically in AI/ML engineering. Proficiency in programming languages such as Python, Java, C++, or similar. Hands-on experience with ML frameworks such as Kubeflow, AI operators, and/or Langchain, with proficiency in Python for AI operations. Experience with MLOps principles, including model deployment, versioning, and/or monitoring in secure environments. Preferred Qualifications Master’s Degree. Focus on on-prem LLM deployments with an emphasis on RAG. Expertise in building large-scale, secure AI/ML platforms with an emphasis on document management and security protocols. Experience with vector databases, such as Pinecone, Weaviate, Milvus, etc. Experience with multi-instance GPUs and containerized AI/ML workflows. Proven ability to collaborate with cross-functional teams, including legal, compliance, and security experts. Understanding of document lifecycle management, particularly in the context of AI model ingestion, classification, and retrieval.

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