Senior Machine Learning Infrastructure Engineer

22 hours ago


Coimbatore, Tamil Nadu, India beBeeInfrastructure Full time ₹ 2,00,00,000 - ₹ 2,50,00,000

As a senior machine learning infrastructure engineer, you will lead the end-to-end design, implementation, and scaling of AI systems.

About the Role:

You will partner with researchers, product teams, and DevOps to transform prototypes into production-ready services that meet strict performance criteria for latency, reliability, and efficiency.

Key Responsibilities:
  • Design and implement scalable machine learning pipelines (training, evaluation, deployment) for large language models, computer vision, and multimodal models
  • Lead efforts in model serving, versioning, automated continuous integration and delivery, and real-time monitoring of AI workflows
  • Build and optimize GPU-backed serving infrastructure targeting sub-100ms latency, 99.9% uptime, and >80% GPU utilization
  • Drive initiatives on model governance, automated drift detection (80% utilization
  • Partner with AI researchers, product managers, and legal teams to align MLOps standards with compliance and roadmap goals
  • Mentor junior engineers, run quarterly knowledge-sharing sessions, own onboarding documentation (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 multiple organizations
  • 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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