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

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

Key Skills
Education

Any Gradute

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