MLOps Engineer

MLOps Engineer

Experience: 3+ Years

Location: Bangalore

Work Mode: Work from Office

About the Role

Bluetick Consultants is looking for an MLOps Engineer to build and operate reliable ML infrastructure and deployment workflows. The role focuses on automation, model lifecycle management, cloud infrastructure, and production reliability across GCP and Azure, including migration of ML workloads to Vertex AI.

What You’ll Do

• Build and automate ML workflows using Apache Airflow / Cloud Composer for data ingestion, preprocessing, and model training.

• Manage MLflow for experiment tracking, model packaging, versioning, and Model Registry.

• Develop and optimize training environments for ML/LLM workloads.

• Design scalable model-serving solutions using FastAPI/Flask, API Gateway, and high-performance inference endpoints.

• Manage Docker and Kubernetes/GKE infrastructure, including auto-scaling GPU/CUDA workloads.

• Implement CI/CD pipelines and automate ML application delivery.

• Monitor model performance, latency, data drift, and infrastructure health.

• Work with Vertex AI services including Workbench, Model Garden, Feature Store, Vertex AI Pipelines, and BigQuery ML.

• Use Terraform to create and maintain reproducible cloud infrastructure.

• Develop production-quality Python code with testing and modular design.

• Work with CDC, Spark/PySpark, and optimize data movement between BigQuery and training environments.

• Implement secure ML environments using IAM, VPC Service Controls, and endpoint security practices.

Key Skills

• 3–5 years of relevant MLOps / ML Engineering experience.

• Strong Python, Docker, Kubernetes, GKE/AKS, and Terraform.

• Hands-on Airflow / Cloud Composer and MLflow experience.

• Experience with GCP, Azure, Vertex AI, BigQuery ML, and Vertex AI Pipelines.

• Knowledge of Kubernetes operators and resource management for ML workloads.

• Experience with FastAPI/Flask, API Gateway, CI/CD, and ML model serving.

• Strong understanding of CDC, Spark/PySpark, IAM, and VPC Service Controls.

• Exposure to LLMOps, foundation models, prompt versioning, and vector database scaling is a plus.

• Experience building or migrating enterprise-scale ML infrastructure across Azure/GCP is preferred.

Preferred: Relevant MLOps/ML Engineering certifications and experience managing production ML platforms at scale.