Data Science Infrastructure Specialist

4 days ago

Surat, Gujarat, India beBeeMachineLearning Full time ₹ 2,40,00,000 - ₹ 2,79,40,000
Lead MLOps Engineer

We are seeking an experienced Senior MLOps Engineer to drive the end-to-end design, implementation, and scaling of our AI infrastructure.

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.
  • 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.).
  • 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 Qualifications:

  • 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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