Job
Description
Key Responsibilities: Collaborate with Data Scientists to develop, optimize, and maintain production-grade AI/ML models. Design and develop MLOps pipelines for efficient model deployment and monitoring. Deploy and configure Kubernetes components for production clusters, including API Gateway, Ingress, Model Serving, Logging, Monitoring, and Cron Jobs . Drive innovation in system design and architecture to enhance model performance and resiliency. Lead operational excellence initiatives in AI/ML, ensuring efficient resource utilization and optimization. Design and implement AI/ML enablement tools, technology, and processes to improve scalability and performance. Utilize observability tools such as Splunk, Prometheus, and Grafana to analyze logs and diagnose system issues. Work with stakeholders, domain experts, and IT leadership to align AI/ML strategies with business objectives. Must-Have Skills: Strong expertise in Generative AI (GenAI) and Natural Language Processing (NLP) . Hands-on experience in designing and developing LLM-based applications . Proficiency in MLOps pipeline development and containerized ML workflows . Experience with Kubernetes cloud offerings (EKS/GKE/AKS) or on-prem Kubernetes solutions (Gravity, MetalK8s). Proficiency in Python, PySpark, PyTorch, Langchain, Docker, and Kubeflow . Solid understanding of data science algorithms (parametric and non-parametric). Hands-on experience in web development and integrating AI models into applications. Strong troubleshooting skills using observability tools such as Splunk, Prometheus, and Grafana . Good-to-Have Skills: Experience working with large-scale AI/ML model deployment in cloud-based distributed computing environments. Knowledge of Red Hat OpenShift, Bit-Bucket, Postman/Insomnia, and Oracle . Experience in forecasting capacity and optimizing AI/ML resources at scale. Strong communication and stakeholder management skills to align business and technical teams.
