Lead operationalization of large-scale AI solutions, owning deployment and AIOps/MLOps for multiple initiatives
Lead, inspire and manage multiple teams of AI Engineers leveraging the AIOps/MLOps techniques to activate AI solutions at enterprise scale, solving real business problems benefitting millions of customers daily
Utilize deep knowledge of software engineering and AI to contribute to the roadmap of core AI capabilities.
Partner with global cross-functional teams including AI, data, platforms, products, analytics, business and compliance
Comprehend complex technical and business problems in high pressure scenarios, connecting the larger enterprise goals to teams tactical deliverables. Effectively define potential value that AI can bring in and communicate technical information to a wide spectrum of cross-functional partners including Product, Data, Infrastructure and Engineering teams.
Attract, recruit and mentor top AI talent to build and maintain industry leading AI Engineering & Ops teams.
Basic Requirements
Master's degree or Ph.D. in a highly quantitative field (Computer Science, Engineering, Physics, Math, Operations Research or related) OR equivalent relevant professional experience
8+ years of experience building and operationalizing ML/AI products. Advanced degrees in relevant fields may be counted towards experience requirements. Including:
4+ years of MLOps/AIOps experience, operationalizing real-time ML/AI models with ultra-low latency and high throughput, as resilient, scalable, cloud native services, with engineering excellence.
4+ years of experience managing technical teams of AI Engineers
Experience managing end to end design reviews and deployment patterns for critical AI applications in one or more of Gen AI, Supervised learning, Unsupervised Learning and Reinforcement Learning domains
Good understanding of Python ecosystem & common frameworks for AI/ML (LangChain, TensorFlow, PyTorch, scikit-learn)
Experience managing AIOps/MLOps processes and execution
Experience building and maintaining ML/AI solutions on Google Cloud Platform (GCP) with production grade pipelines
Preferred Requirements
Multi-cloud and multi-region integration experience
Relevant certifications
Open-source contributions or robust GitHub portfolio
