Manager, AI Engineer ML Ops

Job Description

Role Overview: As a Senior ML Ops Engineer in the Generative AI (GenAI) space, you will play a pivotal role in integrating and deploying models within the cloud alongside our other software assets. You will monitor costs and key performance indicators (KPIs) such as usage, latency, and service provisioning in the cloud to support these models and integrations. As a senior team member, you will oversee and support a team of engineers, which requires experience managing teams and projects, as well as specialization in ML Ops using at least one cloud hyperscaler. What will you do in this role: Integrate and deploy machine learning models in the cloud alongside existing software assets. Monitor the costs, usage, latency, and overall performance of deployed models and cloud services. Support engineering teams requests for model, service, and endpoint provisioning and modification. Provision cloud services that support model deployments and integrations. Manage and support a team of engineers, fostering collaboration, growth, and success. Utilize your specialization and experience to optimize model performance and cloud operations. Stay up-to-date with advancements in machine learning and cloud technologies. What Should you have: Bachelor s degree in Information Technology, Computer Science, or any technology stream. 5+ years of experience in cloud deployment and machine learning operations. Experience with machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, and NumPy). Experience with Natural Language Processing (NLP) and Generative AI. Strong analytical and problem-solving abilities. Effective communication and interpersonal skills to liaise with cross-functional teams. Experience in managing and leading engineering teams. Expertise in DevOps Practices (CI/CD, IaaC) Expertise in cloud computing, resource deployment and management best practices Experience in data analysis, structures, and data ingestion models using cloud services Exposure to service-oriented architecture concepts Experience in Agile Ways of Working and delivery (Scrum, Kanban, Test Driven Development etc.) Experience in managing Software Development Lifecycle (SDLC) principles