Senior AI Infrastructure Engineer

2 days ago

Erode, Tamil Nadu, India beBeeInfrastructure Full time ₹ 3,00,00,000 - ₹ 4,00,00,000
AI Infrastructure Specialist

We are seeking a skilled AI infrastructure specialist to lead the design, implementation, and scaling of our end-to-end AI infrastructure.

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

  • Core Responsibilities:
  • Create scalable ML pipelines (training, evaluation, deployment) for large language models, computer vision, and multimodal models.
  • Design and implement model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows.
  • Built and optimized GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization.
  • Develop 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.
  • Achieve enterprise multi-tenancy by architecting RBAC-driven, isolated ML services to securely serve 100–500+ organizations.
  • Implement observability stacks (Prometheus/Grafana, ELK) for comprehensive insights into 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.
  • Cross-functional leadership: Partner with AI researchers, product managers, and legal experts 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
  • At least 9 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.
Key Skills
  • Vector DB Integration: Qdrant, Pinecone
  • Agent Orchestration: LangChain, LlamaIndex
  • Multi-Tenant Security: RBAC
  • Observability Stacks: Prometheus/Grafana, ELK
  • CI/CD Pipelines: FastAPI, NestJS
Benefits

We offer a supportive environment where team members can grow and develop their skills. Our company is committed to creating an inclusive and supportive workplace where everyone can thrive.

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