MLOps Engineer

Quarks Technosoft Pvt. Ltd.; Bengaluru; On-site; INR 1,200,000 - 2,600,000; Full time

Job summary

Quarks Technosoft Pvt. Ltd. is seeking a hands-on MLOps Engineer to design, implement and manage production-grade ML workflows. You will automate end-to-end ML lifecycle tasks including data prep, training, validation, deployment, monitoring and retraining.

Responsibilities include model lifecycle management, drift detection, and scalable pipeline orchestration using Airflow or Kubeflow, with deployment on AWS, Azure or GCP managed services. Strong Python/SQL and MLflow experience are required.

Qualifications

• Strong hands-on Python and SQL with ML fundamentals.

Responsibilities

• Design and maintain automated ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring and retraining.
• Implement experiment tracking, model registries, versioning and governance.
• Monitor data quality, drift, performance and latency of models in production.
• Build feedback loops for retraining with validation and approvals.
• Develop and manage orchestration with Airflow/Kubeflow/cloud-native tools.
• Implement CI/CD/CT workflows and containerized deployments.
• Leverage AWS/Azure/GCP managed ML services for production workloads.

Skills

Python

SQL

ML fundamentals

MLflow

CI/CD

Docker

Cloud platforms

Education

Bachelor's or Master's in Engineering/CS

Tools

MLflow

Airflow

Kubeflow

Databricks

PySpark

Feast

Evidently AI

WhyLabs

Job description

Primary Focus: End-to-End MLOps, ML Lifecycle Management, Model Monitoring, Drift Detection, Continuous Training and Cloud ML Engineering

Job Overview

We are looking for a hands-on MLOps Engineer experienced in designing, implementing and managing production-grade machine learning workflows. The role involves automating the end-to-end ML lifecycle, including data preparation, training, model validation, deployment, monitoring, feedback integration and retraining.

Key Responsibilities

• End-to-End MLOps: Design and maintain automated ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring and retraining.
• Model Lifecycle Management: Implement experiment tracking, model registries, model versioning, artifact management, model promotion and rollback using MLflow or equivalent platforms.
• Model Monitoring & Drift Detection: Monitor data quality, data/concept drift, model performance degradation, prediction quality, inference latency and operational metrics.
• Feedback Loops & Continuous Training: Build workflows to capture prediction outcomes and ground-truth feedback, evaluate model performance and support retraining with appropriate validation and approval processes.
• ML Pipeline Orchestration: Develop and manage scalable training, inference and retraining pipelines using Airflow, Kubeflow, cloud-native orchestration or equivalent frameworks.
• Model Deployment & CI/CD: Implement automated CI/CD/CT workflows, model serving APIs, containerized deployments, model testing, release management and rollback strategies.
• Cloud ML Engineering: Build and maintain production ML workloads using AWS SageMaker, Azure Machine Learning, GCP Vertex AI or equivalent platforms.
• Production Reliability & Governance: Ensure reproducibility, model lineage, logging, alerting, observability, security, governance and cost-efficient production operations.

Mandatory Technical Skills

• Strong hands-on Python and SQL, with practical understanding of ML fundamentals, feature engineering and model evaluation.
• Experience with production ML pipelines, model deployment, monitoring and lifecycle management.
• Experience with MLflow or equivalent MLOps tools for experiment tracking, model registries and versioning.
• Working experience with data/model drift monitoring, performance evaluation and retraining workflows.
• Experience with CI/CD, Git, Docker and ML pipeline orchestration.
• Hands-on expertise in at least one cloud ecosystem: AWS, Azure or GCP, preferably using its managed ML services.
• Understanding of model serving, production troubleshooting, observability and deployment reliability.

Good-to-Have Skills

• Kubernetes, Terraform/IaC and cloud infrastructure automation.
• Airflow, Kubeflow, PySpark, Databricks, Feast, Evidently AI, WhyLabs or equivalent tools.
• Feature stores, model explainability, champion–challenger testing and canary deployments.
• GenAI/LLMOps, including LLM evaluation, deployment and monitoring.

Experience & Qualifications

• 5–8 years of overall relevant engineering experience, including preferably 2–3 years of hands-on production MLOps/ML Engineering.
• Experience building and maintaining enterprise ML solutions beyond experimentation or standalone API deployments.
• Bachelor's or master's degree in Engineering, Computer Science or a related field.
• Strong problem-solving, troubleshooting and technical communication skills.