int(12923)
Mumbai, India

AI Infrastructure Engineer

Our client is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. We are seeking a Staff-level AI Infrastructure Engineer to architect, build, and operate enterprise-grade AI platforms. This role is pivotal in designing and scaling infrastructure across training, inference, and MLOps, ensuring reliability, security, and performance for production AI systems.

 

Position Type: Permanent

Mode of Interview: Virtual

Experience: 8+ yrs

Notice Period: Immediate

Skills: Hands-on experience in AI Engineer, Platform Engineer, MLOps, AWS, Kubernetes, Python (Mandate)

 

Please contact Radhika or email your cv directly in word format with job reference number JOB: 16160 to Radhika@theedgepartnership.com

 

ONLY CANDIDATES SERVING THE NOTICE PERIOD CAN SHARE YOUR UPDATED CV.

 

Please note that due to the high number of applications only shortlisted candidates will be contacted. If you do not hear from us in the next 5 business days, we regret to inform you that your application for this position was unsuccessful.

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Key responsibilities

  • Architect AI Infrastructure across model training, inference pipelines, and MLOps workflows.
  • Design and implement scalable platforms leveraging Kubernetes, cloud-native services, and CI/CD automation.
  • Ensure reliability, security, and scalability of AI systems in production.
  • Collaborate with data scientists and ML engineers to optimize workflows and accelerate model deployment.
  • Develop automation tools for monitoring, logging, and performance optimization.
  • Drive best practices in infrastructure-as-code, container orchestration, and distributed systems.
  • Mentor junior engineers and contribute to technical leadership across AI infrastructure initiatives.

Role requirements

Staff AI Infrastructure Engineer / AI Infrastructure Consultant.

  • Location: Mumbai, India, Hybrid (3 Days).
  • Staff-level platform engineer for production AI systems.
  • Architects AI infrastructure across training, inference, and MLOps.
  • Deep Kubernetes, cloud, and CI/CD experience.
  • Owns reliability, security, and scalability of AI platforms.
  • Strong Go or Python skills with hands-on AI coding assistants.
  • 8 to 12 plus years building and operating enterprise-grade platforms.