Senior ML Engineer

1 day ago

Thiruvananthapuram, Kerala, India beBeeMachineLearning Full time ₹ 1,80,00,000 - ₹ 2,50,00,000

Unlock a world of possibilities as a Senior ML Engineer in the field of Artificial Intelligence.

Key Responsibilities
  • Design and implement resilient Machine Learning services with clear Service Level Objectives (SLOs), runbooks, and fast, safe rollbacks.
  • Buid automation-first delivery pipelines: reproducible builds, layered tests, and environment promotion via GitLab CI and Terraform-based Infrastructure as Code (IaC).
  • Engineer scalable serving architecture for batch and real-time inference on EKS/ECS/Lambda and Databricks Model Serving with probes, autoscaling, and canary/blue-green deployments.
  • Instrument end-to-end observability (data, model, system); detect drift/regressions; lead incidents and post-mortems that drive durable fixes.
  • Partner across teams to translate requirements into designs, Architecture Decision Records (ADRs), and change plans; balance security, privacy, cost, and performance tradeoffs.
  • Continuously reduce toil through automation, optimize model/GPU/LLM cost, and evolve templates/playbooks for repeatable delivery.

Mandatory Skills:

  • Bachelor's degree in Computer Science, Engineering, Data Science or a related field and 3+ years of relevant experience; or High School Diploma/General Education Degree and 6+ years of relevant experience in lieu of Bachelor's Degree.
  • 3+ years operating ML systems in production.
  • Experience with Python for ML engineering and GitLab CI for ML/GenAI.
  • Experience deploying and operating compute for ML and secure data access patterns.
  • Experience implementing MLflow tracking, model registry & governed promotion, packaging & deployment to multi-target runtimes.
  • Experience operating real-time + batch/streaming inference workloads, ML observability, layered testing (unit/integration), workflow orchestration, and cost optimization.

Preferrred Qualifications:

  • Advanced GitLab CI and Terraform skills.
  • Real-time serving expertise and SLO-driven rollback/automation.
  • Databricks governance knowledge and feature platform approval/reuse workflows.

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