Machine Learning Specialist

23 hours ago


Bengaluru, Karnataka, India beBeeMachineLearning Full time ₹ 1,50,00,000 - ₹ 2,15,00,000

Machine Learning Engineer

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We are seeking a skilled Machine Learning Engineer to join our team. In this role, you will be responsible for the productionization of machine learning models and ensuring the reliability and efficiency of our services.

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  • Key Responsibilities:
  • Develop and deploy machine learning models using Python and related technologies.
  • Design and implement automation pipelines for model training and deployment.
  • Collaborate with cross-functional teams to translate requirements into designs and change plans.
  • Partner with security, privacy, and compliance teams to ensure that our ML systems meet the required standards.

Qualifications:

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  • 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 (MLOps).
  • 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.
  • Experience deploying and operating compute for ML (EKS/ECS/Lambda), and secure data access patterns (S3/VPC/IAM/KMS, private endpoints).
  • 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.
  • Experience designing and implementing IAM least-privilege, secrets/key management for CI/CD pipelines; privacy and compliance awareness.

Preferred Qualifications:

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  • Advanced GitLab CI (dynamic child pipelines, components, cross-project triggers, security scans, compliance gates).
  • Advanced Terraform (policy-as-code, gated plan/apply, environment promotion).
  • Advanced real-time serving (multi-tenant routing, dynamic model loading) and SLO-driven rollback/automation.
  • Databricks governance (Unity Catalog, lineage) and feature platform approval/reuse workflows.


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