Principal Machine Learning Engineer

2 weeks ago

Kanpur, Uttar Pradesh, India beBeeMachineLearning Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Our ideal candidate will lead the productionization of machine learning models from tracked experiments to governed releases, ensuring resilient services with clear service level objectives (SLOs), runbooks, and fast, safe rollbacks.

Key Responsibilities:
  • Automation Engineering
  • Build scalable serving architectures for batch and real-time inference on Amazon Elastic Container Service for Kubernetes (EKS), Amazon ECS, AWS Lambda, and Databricks Model Serving, utilizing probes, autoscaling, and canary/blue-green deployments.
  • Instrument end-to-end observability for data, models, and systems; detect drift and regressions; lead incidents and post-mortem analyses to drive durable fixes.
  • Partner across teams to translate requirements into designs, Architecture Decision Records (ADRs), and change plans, balancing security, privacy, cost, and performance trade-offs.
  • Continuously reduce toil through automation, optimize model/GPU/Large Language Model (LLM) costs, and evolve templates/playbooks for repeatable delivery.

Required Skills and Qualifications:

  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related field and 3+ years of relevant experience in machine learning operations, or High School Diploma/General Education Degree and 6+ years of relevant experience in lieu of Bachelor's Degree.
  • 3+ years operating machine learning systems in production.
  • Experience with Python for machine learning engineering, including packaging, typing, testing, and performance optimization.
  • Proficiency in developing GitLab CI pipelines for machine learning and Generative AI, including multi-stage pipelines, artifacts, evaluation/security gates, and secure packaging/containerization.
  • Experience deploying and operating compute infrastructure for machine learning, including EKS/ECS/Lambda, and secure data access patterns using S3/VPC/IAM/KMS and private endpoints.
  • Experience implementing MLflow tracking, model registry, and governed promotion, packaging/deployment to multi-target runtimes.
  • Experience operating real-time and batch/streaming inference workloads, machine learning observability, layered testing, workflow orchestration, and cost optimization.
  • Experience designing and implementing IAM least-privilege and secrets/key management for CI/CD pipelines, with awareness of privacy and compliance regulations.

Preferred Qualifications:

  • Advanced GitLab CI skills, including dynamic child pipelines, components, cross-project triggers, security scans, and compliance gates.
  • Expertise in Terraform, including policy-as-code, gated plan/apply, and environment promotion.
  • Advanced real-time serving capabilities, including multi-tenant routing and dynamic model loading, and SLO-driven rollback/automation.

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