Senior AI Infrastructure Engineer

7 days ago

Dindigul, Tamil Nadu, India beBeeMlops Full time ₹ 1,24,80,000 - ₹ 2,01,20,000

About this Role:

We are seeking a strategic Senior MLOps Engineer to lead the end-to-end design, implementation, and scaling of our AI infrastructure.

The ideal candidate will partner with researchers, product teams, and DevOps to turn prototypes into production services that meet strict SLAs for latency, reliability, and cost efficiency.

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.

Prior Experience:

  • A strong portfolio of projects demonstrating expertise in MLOps, AI infrastructure, and cloud-based solutions.
  • A Master's or Ph.D. in CS/AI and relevant certifications.
  • Excellent communication and interpersonal skills, with the ability to drive consensus across functions.

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