Chief Machine Learning Operations Specialist

1 week ago

Kanpur, Uttar Pradesh, India beBeeMlops Full time ₹ 19,30,400 - ₹ 23,16,000
Job Description

We are seeking a highly skilled Senior MLOps Engineer to lead our AI infrastructure design team. The ideal candidate will have expertise in cloud platforms, Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow.

The successful candidate will be responsible for designing and implementing scalable ML pipelines, leading efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows.

  • Key Responsibilities:
  • Design and implement scalable ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models
  • Lead efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows
  • Build and optimize GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization
  • Drive initiatives on model governance, automated drift detection (≤10% false positives), and data-management best practices
  • Integrate vector databases (Qdrant, Pinecone) for low-latency semantic retrieval, and build agentic workflows using LangChain or similar frameworks
  • Architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations
  • Design Prometheus/Grafana dashboards, ELK/Fluentd logging pipelines, and alerting for all ML workloads
  • Maintain CI/CD pipelines for Python (FastAPI) and TypeScript (NestJS) inference services
  • Define and track SLAs/SLOs, optimize cloud spend by ≥ 20% year-over-year, and ensure GPU clusters operate at > 80% utilization
  • Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals
Requirements

To succeed in this role, you will need:

  • 9–14 years in software engineering, including ≥ 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.)
  • Proven track record building reliable, scalable ML systems in production
Critical Skills

The following skills are essential for this role:

  • Vector DB integration (Qdrant, Pinecone)
  • Agent orchestration (LangChain, LlamaIndex)
  • Multi-tenant security and RBAC
  • Observability stacks (Prometheus/Grafana, ELK)
  • CI/CD for FastAPI/NestJS services

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