Expert AI Infrastructure Specialist

1 day ago

Bhavnagar, Gujarat, India beBeeMLOps Full time ₹ 30,00,000 - ₹ 40,00,000
Senior MLOps Engineer

Location: Mumbai, India

Experience Level:

At least 9 years in software engineering, including at least 4 years in MLOps or ML infrastructure.

The ideal candidate will have a strong background in cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow.

Responsibilities:

  1. Design and implement scalable ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models.
  2. Lead efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows.
  3. Build and optimize GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization.
  4. Drive initiatives on model governance, automated drift detection (≤10% false positives), and data-management best practices.
  5. Integrate vector databases (Qdrant, Pinecone) for low-latency semantic retrieval, and build agentic workflows using LangChain or similar frameworks.
  6. Architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations.
  7. Design Prometheus/Grafana dashboards, ELK/Fluentd logging pipelines, and alerting for all ML workloads.
  8. Maintain CI/CD pipelines for Python (FastAPI) and TypeScript (NestJS) inference services.
  9. Define and track SLAs/SLOs, optimize cloud spend by ≥ 20% year-over-year, and ensure GPU clusters operate at > 80% utilization.
  10. Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals.
  11. 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 of experience in software engineering, including at least 4 years in MLOps or ML infrastructure.
  • Strong expertise in cloud platforms, containerization, orchestration tools, and DevOps practices.
  • Proficiency in Python, TypeScript, and infrastructure-as-code (Terraform, Helm, etc.).
  • Proven track record building reliable, scalable ML systems in production.

Plus These 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 skills.

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