Sr AI/ML Engineer - CX

Cisco
San Jose, California, US$200k – $255kPosted 18 February 2026

Job Description

The application window is expected to close on: 02/18/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received. Meet the Team CX AI Incubation team is part of CX and is focused on identifying and building breakthrough emerging solutions to support the diverse requirements of the CX organization and our customers. Are you ready to be at the forefront of AI innovation? At Cisco CX, you will design and develop transformative AI capabilities and AI-driven solutions that redefine how customers interact with technology. From delivery intelligence to network automation and intelligence on edge, your work will power next-generation AI applications that make a tangible difference. You will collaborate with passionate experts to build scalable, responsible, and cutting-edge AI models, including Large and Small Language Models, leveraging the latest advances in generative AI, various post training techniques including reinforcement learning, and more. This is your chance to push the boundaries of AI technology and accelerate your career in a vibrant, inclusive environment that celebrates creativity and impact. Your Impact Join Cisco’s Customer Experience (CX) AI Incubation team to design and develop innovative AI-driven solutions that transform customer engagement and operational efficiency. You will work on cutting-edge use cases including delivery intelligence, knowledge management, network test automation, infrastructure testing, intelligence on edge, and DevOps automation. This role requires strong expertise in AI/ML, software development, and a passion for applying AI to real-world challenges. Join us to shape the future, cultivate lasting relationships, and ensure every interaction counts. What You’ll Do Contribute to the design and development of AI–ML based services collaborating with product management and engineering teams to deliver impactful, scalable AI solutions. Analyze data, develop, validate, and deploy machine learning models that drive measurable improvements in operational efficiency and automation. Build and fine-tune model architectures for natural language and perception-based tasks, applying best practices in model optimization and evaluation. Work with techniques such as transformer architecture, distillation, and reinforcement learning to improve model performance, scalability, and reliability. Support the training and fine-tuning of Large and Small Language Models (LLMs and SLMs) for domain- and task-specific applications. Collaborate on the development and deployment of AI agents and applications, ensuring responsible, safe, and explainable AI behavior. Stay current with emerging AI technologies and research to continuously improve solution quality and technical capability. Contribute to best practices in model development and deployment. Minimum Qualifications Bachelor’s degree in computer science, Machine Learning, Mathematics, Statistics, or related field with 6+ years of software engineering experience, or Master’s degree in a related field with 3+ years of experience. Experience in Python, Java or C++, with experience in model development, training, and deployment. Experience with leading machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn along with data manipulation tools essential for the AI lifecycle. Experience developing Large Language Models (LLMs), Natural Language Processing (NLP), or Generative AI techniques. Experience working in cross-functional teams, deliver results in fast-paced environments, and effectively communicate technical concepts. Preferred Qualifications Experience working on (SLM) Small Language Model development. Experience with Cisco networking and security technologies Knowledge of network automation and AI-based anomaly detection. Familiarity with AI-driven DevOps automation and model observability. Exposure to edge computing environments. Experience on various AI cloud platforms such as AWS Sag ... (truncated, view full listing at source)
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