Leading MLOps Engineer

3 days ago

Rajkot, Gujarat, India beBeeMlEng Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Senior MLOps Engineer Position Overview

We are seeking a seasoned Senior MLOps Engineer to spearhead the development and deployment of our AI infrastructure.

This role requires collaboration with researchers, product teams, and DevOps to transform prototypes into production-ready services that meet stringent SLAs for latency, reliability, and cost efficiency.

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.Mentor junior engineers, run quarterly brown-bags, own onboarding docs (upskill 5+ engineers/quarter), and publish ≥ 1 open-source contribution or talk annually.Requirements:
  • 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.).
  • A proven track record building reliable, scalable ML systems in production.

Critical Skills:

  • 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

Preferred:

  • Master's/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD.
  • Prior experience at AI-focused startups or enterprises scaling ML for 100–500 orgs.
  • Understanding of low-latency streaming inference or agent-based LLM systems.
  • Excellent written and verbal communication, and a proven ability to drive consensus across functions.

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