AI Scientist

• 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

What do you need to succeed?

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