MLOps Engineer

8 hours ago

Hyderabad, Telangana, India Spydra Full time ₹ 15,00,000 - ₹ 25,00,000 per year

About The Role
Were looking for an MLOps Engineer to build and operate reliable, secure, and scalable ML/LLM infrastructurefrom data ingestion and training pipelines to model serving, monitoring, and continuous improvement.

Youll partner with Data Science, Platform, and Security teams to ship models to production with strong SLAs, observability, and cost control.

Responsibilities

  • Productionize models end-to-end : automate data ingestion, feature engineering, training, evaluation, packaging, and deployment (batch & real-time).
  • Model serving & orchestration : design/operate low-latency model endpoints and batch jobs using Kubernetes, Docker, job schedulers, and serving frameworks.
  • CI/CD for ML : implement reproducible pipelines (code, data, features, models) with unit/integration tests, approvals, and canary/blue-green rollouts.
  • Monitoring & reliability : build drift, performance, and data-quality monitors; set alerts and on-call runbooks; drive incident response and postmortems.
  • Observability : instrument tracing/logging/metrics (e.g., OpenTelemetry, Prometheus, Grafana) across data flows and model requests.
  • Model registry & governance : manage lineage, versioning, approvals, and audit trails; enforce security (IAM, secrets management) and compliance controls.
  • Cost & capacity management : optimize GPU/CPU usage, autoscaling, caching, batching, quantization, and instance right-sizing.
  • LLM & RAG pipelines (nice if applicable) : stand up vector databases, retrieval flows, prompt/version management, guardrails, and evaluations.
  • Collaboration & enablement : create templates, docs, and self-service tooling for data scientists and app teams.

Required Qualifications

  • 3-7 years in MLOps/Platform/DevOps/SRE roles supporting ML in production.
  • Strong with Python and one of Go/TypeScript/Bash; proficiency in Docker and Kubernetes.
  • Experience building ML pipelines with tools like CI/CD expertise (GitHub Actions/GitLab/Jenkins/Argo), including artifact/version management and automated testing.
  • Data stack : object storage (S3/GCS/Azure Blob), data warehouses/lakes, message queues/streams (Kafka/PubSub), and caching layers.
  • Monitoring/observability : Prometheus, Grafana, ELK/EFK, alerting (PagerDuty/VictorOps), tracing (OpenTelemetry/Jaeger).
  • Security fundamentals : IAM, network policies, secrets (Vault/SSM), image signing, SBOMs.
  • Solid understanding of ML lifecycle : data versioning, feature stores, experiment tracking, evaluation, and rollback.

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