Description
Join Cisco's team that's advancing AI/ML capabilities by designing innovative algorithms and neural network architectures for natural language processing, machine perception, and Generative AI. You will drive the development of scalable, production-ready machine learning solutions that power Cisco's products and deliver measurable business impact.
Responsibilities:
- Analyse Cisco and external datasets to identify opportunities, formulate hypotheses, and translate business needs into feasible ML solutions.
- Design and optimise advanced models using transformers, convolutional neural networks, knowledge distillation, student-teacher frameworks, GANs, and other emerging techniques.
- Lead the exploration and adoption of state-of-the-art algorithms and architectures for complex, business-critical ML applications.
- Establish technical vision and model design practices across functions and product portfolios, guiding teams toward scalable and reliable solutions.
- Automate ML workflows spanning model selection, training, inference, and deployment to improve efficiency and performance.
- Architect specialised data pipelines and optimisation techniques to enhance model training, accuracy, and computational efficiency.
- Champion CI/CD-driven model integration, deployment, lifecycle management, and continuous improvement.
Minimum Qualifications:
- Bachelor's degree with 12+ years, Master's degree with 8+ years, or PhD with 5+ years of relevant experience.
- Strong expertise in machine learning, deep learning, and neural network architecture design for NLP, machine perception, or related applications.
- Hands-on experience developing and optimising advanced ML models, including transformer-based and convolutional architectures, knowledge distillation, student-teacher models, and GANs.
- Proficiency in model training, inference optimisation, data preparation, and specialised data-splitting strategies.
- Experience automating ML workflows, improving model performance, and addressing scalability, efficiency, and reliability challenges.
Preferred Qualifications:
- Demonstrated technical leadership in shaping system architecture and mentoring engineers on ML platforms and cloud-native best practices.
- Strong systems-thinking skills to balance model quality, scalability, reliability, latency, and cost in production environments.
- Proven ability to evaluate emerging AI/ML approaches and translate research into scalable, production-ready solutions.
- Passion for staying current with advancements in AI/ML, Generative AI, and next-generation model architectures.