MLOps Engineer

Unico Connect LLP.; Mumbai; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

Unico Connect LLP. in Mumbai is looking for an MLOps engineer to operate model deployment pipelines on GCP and AWS. The ideal candidate should have a Bachelor's degree in Computer Science and 1-3 years of relevant experience.

Key responsibilities include maintaining CI/CD for ML, developing observability measures, and partnering closely with AI engineers. Candidates should be comfortable with Python, Docker, and Kubernetes, and possess strong documentation skills.

Qualifications

• 1-3 years of experience in MLOps, DevOps, or backend engineering with ML exposure.
• Strong Python; comfortable reading PyTorch or TensorFlow code.
• Hands-on with Docker, Kubernetes, and at least 1 cloud (GCP or AWS).
• Familiarity with MLflow or Weights & Biases for experiment tracking.
• Working knowledge of CI/CD with GitHub Actions or Cloud Build.
• Basic understanding of evaluation metrics and statistical significance.
• Strong fundamentals in observability concepts including SLOs and alerting.

Responsibilities

• Operate model deployment pipelines on GCP Vertex AI, AWS SageMaker, or self-hosted Kubernetes.
• Maintain CI/CD for ML including data validation, model training, and deployment.
• Build observability for production models covering latency, cost, and drift.
• Implement A/B and canary rollouts for ML and LLM features.
• Maintain model registries and experiment tracking with MLflow or Weights & Biases.
• Govern model versioning and rollback procedures.

Skills

Python

CI/CD

Docker

Kubernetes

GCP

AWS

MLflow

Weights & Biases

Terraform

Statistical significance

Education

Bachelor's degree in Computer Science or a related field

Tools

PyTorch

TensorFlow

Job description

Responsibilities

• Operate model deployment pipelines on GCP Vertex AI, AWS SageMaker, or self-hosted Kubernetes
• Maintain CI/CD for ML including data validation, model training, evaluation gates, and deployment
• Build observability for production models covering latency, cost, drift, and hallucination rate
• Implement A/B and canary rollouts for ML and LLM features
• Maintain model registries, feature stores, and experiment tracking with MLflow or Weights & Biases
• Partner with AI Engineers to harden development to staging to production flows
• Govern model versioning, prompt versioning, and rollback procedures
• Author runbooks for common ML production incidents including drift and cost spikes
• Maintain Terraform modules and Kubernetes manifests for shared ML platform components
• Participate in on-call rotations for ML platform incidents

Qualifications

• Bachelor's degree in Computer Science or a related field
• 1-3 years of experience in MLOps, DevOps, or backend engineering with ML exposure
• Strong Python; comfortable reading PyTorch or TensorFlow code
• Hands-on with Docker, Kubernetes, and at least 1 cloud (GCP or AWS)
• Familiarity with MLflow, Weights & Biases, or comparable experiment tracking
• Working knowledge of CI/CD with GitHub Actions or Cloud Build
• Comfort reading and modifying Terraform modules
• Basic understanding of model evaluation metrics and statistical significance
• Strong fundamentals in observability concepts including SLOs and alerting
• Excellent written communication for runbook and incident documentation