AI Infrastructure Leader

12 hours ago

Dombivli, Maharashtra, India beBeeMlengineer Full time ₹ 2,50,00,000 - ₹ 3,50,00,000
About the Role">

We are seeking a skilled Senior MLOps Engineer to lead our AI infrastructure team. As a senior member of our team, you will partner with researchers, product teams, and DevOps engineers to design, implement, and scale our end-to-end AI infrastructure.

Responsibilities include designing scalable ML pipelines for large language models, computer vision, and multimodal models, as well as leading efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows.

You will also be responsible for building and optimizing GPU-backed serving infrastructure targeting low latency, high uptime, and high GPU utilization.

Furthermore, you will drive initiatives on model governance, automated drift detection, and data-management best practices.

Additional responsibilities include integrating vector databases for low-latency semantic retrieval and building agentic workflows using LangChain or similar frameworks.

Lastly, you will architect RBAC-driven, isolated ML services to securely serve multiple organizations.

Requirements

To succeed in this role, you will need:

  • 9-14 years of software engineering experience, including at least 4 years in MLOps or ML infrastructure
  • Strong expertise in cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow
  • Experience with inference frameworks (Triton, TensorFlow Serving, BentoML, TorchServe)
  • Familiarity with distributed training, workload schedulers, and GPU-cluster orchestration
  • Proficiency in Python, TypeScript, and infrastructure-as-code (Terraform, Helm, etc.)
  • A proven track record of building reliable, scalable ML systems in production

Benefits

This is an excellent opportunity to work with a talented team and contribute to the development of cutting-edge AI technology. We offer a competitive salary and benefits package, as well as opportunities for professional growth and development.

Preferred Qualifications

Prior experience at AI-focused startups or enterprises scaling ML for multiple organizations is preferred. A Master's/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD are also desirable.

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