Mastery of Machine Learning Engineering

1 week ago

Shimoga, Karnataka, India beBeeEngineer Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Title: Senior MLOps Engineer

Key Responsibilities:

As a Senior MLOps Engineer, you will be responsible for leading the design, implementation, and scaling of AI infrastructure. This includes partnering with researchers, product teams, and DevOps to turn prototypes into production services that meet strict SLAs for latency, reliability, and cost efficiency.

  • Design and Implement Scalable ML Pipelines: Develop efficient ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models using cloud platforms like AWS/GCP/Azure, Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow.
  • Model Serving & CI/CD: Lead efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows using Model Serving, inference frameworks like Triton, TensorFlow Serving, BentoML, TorchServe, and distributed training tools.
  • Inference-as-a-Service: Build and optimize GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization using GPU-cluster orchestration and workload schedulers.
  • Governance & Drift Detection: Drive initiatives on model governance, automated drift detection (≤10% false positives), and data-management best practices using vector databases like Qdrant, Pinecone, and agentic workflows.
  • Vector Search & Agent Orchestration: Integrate low-latency semantic retrieval using vector databases and build agentic workflows using LangChain or similar frameworks.
  • Enterprise Multi-Tenancy: Architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations using multi-tenant security and RBAC.
  • Observability & Logging: Design Prometheus/Grafana dashboards, ELK/Fluentd logging pipelines, and alerting for all ML workloads.
  • Cross-Functional Leadership: Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals.
  • Mentorship & Community: 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, containerization technologies, and machine learning frameworks.
  • Experience with inference frameworks, distributed training, workload schedulers, and GPU-cluster orchestration.
  • Proficiency in Python, TypeScript, and infrastructure-as-code tools.
  • Proven track record building reliable, scalable ML systems in production.

Plus These Critical Skills:

  • Vector DB integration.
  • Agent orchestration.
  • Multi-tenant security and RBAC.
  • Observability stacks.
  • CI/CD for FastAPI/NestJS services.

Preferred:

  • Master's/PhD in CS/AI and certifications like 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.

Benefits:

This role offers an excellent opportunity to grow professionally while collaborating effectively across functions to achieve business objectives. The ideal candidate will have strong technical skills, leadership abilities, and a passion for innovation and learning.

What We Value:

We value diversity, equity, and inclusion and welcome applications from talented individuals from diverse backgrounds.

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