Job Description: Engineer advanced AI capabilities to power Research Digitization, Banking and Global Market solutions Evaluation and subsequent implementation of Data/Model Parallelism libraries and techniques, AI Observability & Monitoring solutions, Vector databases and inference engines Deploy, manage, performance tune and scale containerized applications using Kubernetes. Architect clusters leveraging fit for purpose hardware for AI workloads Provide subject matter expertise in distributed and parallel computing. Analyze ML and data processing workloads to identity latency contributors, inefficient compute utilization and provide remediation recommendations Automate AI infrastructure provisioning Create reports for AI infrastructure usage and cost reports Collaborate with multi-functional geographically distributed teams to drive cutting edge AI infrastructure and solutions Must Have Minimum of 2 years of hands-on AI Engineering experience Degree in Computer Science or Engineering Experience building, scaling, managing and monitoring ML pipelines Experience with Python, Kubernetes, distributed computing Solid understanding of machine learning, generative AI, agents, multi-agent collaboration, MCP servers, with focus on real-world implementation Proficient in CI/CD principles, version control and best practices for deploying AI workloads to production. Nice to Have Azure Databricks, Spark, Snowflake, NVIDIA NIM, MLFlow Experience building and deploying MCP Servers, AI Agents Experience with any vector database. Experience with Terraform