AI Infrastructure Developer

4 days ago

Davangere, Karnataka, India beBeeMlOps Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Senior MLOps Engineer

Our organization is seeking a strategic Senior MLOps Engineer to oversee the development, implementation, and scaling of our AI infrastructure.

About the Role

You will collaborate with researchers, product teams, and DevOps to transform 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.
  • Develop 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 (= 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 = 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
  • 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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