Senior AI Systems Engineer

5 days ago

Bikaner, Rajasthan, India beBeeMLOps Full time US$ 1,80,000 - US$ 2,50,000

Overview:

We are seeking a seasoned MLOps professional to join our team. As a Senior MLOps Engineer, you will be responsible for ensuring the resilience and scalability of our machine learning models.

You will productionize models by translating tracked experiments into governed releases with clear Service Level Objectives (SLOs), runbooks, and fast, safe rollbacks.

Key Responsibilities:
  • Build automation-first delivery pipelines using GitLab CI and Terraform-based Infrastructure as Code (IaC) to ensure reproducible builds, layered tests, and environment promotion.
  • Design and engineer scalable serving solutions for batch and real-time inference on Amazon Elastic Container Service (ECS), AWS Lambda, and Databricks Model Serving, with probes, autoscaling, and canary/blue-green deployments.
  • Instrument end-to-end observability, detect drift and regressions, lead incidents and post-mortem analysis to drive durable fixes.
  • Collaborate across teams to translate requirements into designs, Architecture Decision Records (ADRs), and change plans, balancing security, privacy, cost, and performance trade-offs.

Requirements:

  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, along with 3+ years of experience in MLOps or equivalent combination of education and experience.
  • 3+ years operating ML systems in production, including experience with Python for ML engineering.
  • Experience developing GitLab CI pipelines for ML/GenAI and Terraform for ML/GenAI, secure packaging & containerization.
  • Experience deploying and operating compute for ML, and secure data access patterns.
  • Experience implementing MLflow tracking, model registry, and governed promotion, packaging & deployment to multi-target runtimes.
Preferred Qualifications:
  • Advanced GitLab CI pipeline management, including dynamic child pipelines, components, cross-project triggers, security scans, and compliance gates.
  • Expertise in advanced Terraform features, such as policy-as-code, gated plan/apply, and environment promotion.
  • Real-time serving expertise, including multi-tenant routing, dynamic model loading, and SLO-driven rollback/automation.

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