Principal AI Systems Engineer

5 days ago

Erode, Tamil Nadu, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 17,50,000
Senior MLOps Engineer

We are seeking a highly skilled Senior MLOps Engineer to join our team. In this role, you will be responsible for productionizing models, building automation-first delivery pipelines, and ensuring the scalability and reliability of our machine learning systems.

About the Role

This is a senior-level position that requires a strong background in MLOps, software engineering, and cloud computing. You will work closely with our data science team to design, implement, and deploy machine learning models that meet our business requirements.

Key Responsibilities
  • Productionize machine learning models to ensure resilient services with clear Service Level Objectives (SLOs) and runbooks
  • Build automation-first delivery pipelines using GitLab CI/CD and Terraform-based Infrastructure as Code (IaC)
  • Engineer scalable serving using 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 to detect drift/regressions and lead incident response efforts
  • Partner across teams to translate requirements into designs, Architecture Decision Records (ADRs), and change plans while balancing security, privacy, cost, and performance tradeoffs
Requirements
  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related field with 3+ years of relevant experience; or High School Diploma/General Education Degree with 6+ years of relevant experience
  • Operating ML systems in production (MLOps) for at least 3 years
  • Experience with Python for ML engineering (packaging, typing, testing, performance)
  • Experience developing GitLab CI for ML/GenAI (multi-stage pipelines, artifacts, evaluation/security gates) and Terraform for ML/GenAI (reusable modules, drift detection); secure packaging & containerization
  • Deploying and operating compute for ML (EKS/ECS/Lambda), and secure data access patterns (S3/VPC/IAM/KMS, private endpoints)
  • Implementing MLflow tracking, model registry & governed promotion, packaging & deployment to multi-target runtimes
  • Real-time + batch/streaming inference workloads, ML observability, layered testing (unit/integration), workflow orchestration, and cost optimization experience required
Preferred Qualifications
  • Advanced GitLab CI knowledge (dynamic child pipelines, components, cross-project triggers, security scans, compliance gates)
  • Advanced Terraform knowledge (policy-as-code, gated plan/apply, environment promotion)
  • Real-time serving (multi-tenant routing, dynamic model loading) and SLO-driven rollback/automation expertise
  • Databricks governance (Unity Catalog, lineage) and feature platform approval/reuse workflows

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