Transforming Machine Learning Models into High-Performance Production Systems

2 weeks ago

Jamnagar, Gujarat, India beBeeMLOps Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Machine Learning Engineer - Productionization Specialist

As a Machine Learning Engineer specializing in productionization, you will be responsible for ensuring the seamless integration of machine learning models into our production environment. This includes developing and maintaining automation-first delivery pipelines, scalable serving architectures, and robust monitoring systems.

Key Responsibilities:
  1. Develop and maintain automated delivery pipelines using GitLab CI and Terraform-based infrastructure-as-code to ensure efficient model deployment and version control.
  2. Design and implement scalable serving architectures for batch and real-time inference on EKS/ECS/Lambda and Databricks Model Serving to optimize model performance and reduce latency.
  3. Instrument end-to-end observability and monitoring systems to detect drift and regression, ensuring data quality and model accuracy.
  4. Collaborate with cross-functional teams to translate business requirements into technical designs, ADRs, and change plans, driving innovation and improvement.
  5. Continuously optimize model and GPU cost through automation and resource allocation strategies, reducing toil and improving overall efficiency.

Minimum Qualifications:

  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, and 3+ years of relevant experience.
  • 3+ years operating machine learning systems in production, with expertise in MLOps.
  • Strong proficiency in Python for ML engineering, including packaging, typing, testing, and performance optimization.
  • Experience developing GitLab CI for machine learning, including multi-stage pipelines, artifacts, evaluation, and security gates.
  • Expertise in deploying and operating compute resources for ML, including EKS/ECS/Lambda, and secure data access patterns.
  • Knowledge of MLflow tracking, model registry, governed promotion, packaging, and deployment to multi-target runtimes.
  • Experience operating real-time and batch/streaming inference workloads, ML observability, layered testing, workflow orchestration, and cost optimization.

Preferred Qualifications:

  • Advanced GitLab CI, including dynamic child pipelines, components, cross-project triggers, security scans, and compliance gates.
  • Expertise in advanced Terraform, including policy-as-code, gated plan/apply, environment promotion, and reusable modules.
  • Experience with real-time serving architectures, including multi-tenant routing and dynamic model loading.
  • Databricks governance, Unity Catalog, lineage, and feature platform approval/reuse workflows.

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