Description

You will engineer advanced AI capabilities to support Research Digitization, Banking, and Global Market solutions.

Responsibilities

• Evaluate and implement Data/Model Parallelism libraries, AI Observability, monitoring solutions, vector databases, and inference engines.
• Deploy, manage, and performance tune containerized applications using Kubernetes.
• Architect clusters using hardware optimized for AI workloads.
• Analyze ML and data processing workloads to identify latency and compute inefficiencies.
• Automate AI infrastructure provisioning and generate usage and cost reports.

Required Skills

• Minimum 2 years of hands-on AI Engineering experience.
• Degree in Computer Science or Engineering.
• Experience building, scaling, managing, and monitoring ML pipelines.
• Proficiency in Python and Kubernetes.
• Experience with distributed computing and parallel computing.
• Solid understanding of machine learning, generative AI, agents, multi-agent collaboration, and MCP servers.
• Proficiency in CI/CD principles and version control for production AI workloads.

Preferred Skills

• Experience with Azure Databricks, Spark, Snowflake, NVIDIA NIM, or MLflow.
• Experience building and deploying MCP Servers and AI Agents.
• Experience with vector databases or Terraform.

Key Skills

Education

Any Gradute