MLOps Engineer

Quarks Technosoft Pvt. Ltd.; Dadri, Pune District, Bengaluru; On-site; INR 2,000,000 - 3,600,000; Full time

Job summary

Quarks Technosoft Pvt. Ltd. in India seeks a hands-on MLOps Engineer to architect and manage production-grade ML workflows across the full lifecycle.

The role emphasizes automating data preparation, training, validation, deployment, monitoring and retraining to ensure robust ML solutions. The candidate will implement experiment tracking, model registries, drift detection, and canary deployments while leveraging CI/CD and cloud ML services on AWS/Azure/GCP.

Qualifications

• Strong hands-on Python and SQL with ML fundamentals.
• Experience with production ML pipelines and lifecycle management.
• Experience with MLflow or equivalent MLOps tools for experiment tracking.
• Experience with data/model drift monitoring and retraining workflows.
• CI/CD, Git, Docker and ML pipeline orchestration experience.
• Hands-on cloud experience (AWS, Azure or GCP) using managed ML services.
• Understanding of model serving, production troubleshooting and observability.

Responsibilities

• End-to-End MLOps: design and maintain automated ML pipelines (data ingestion, feature engineering, training, validation, deployment, monitoring, retraining).
• Model Lifecycle Management: experiment tracking, registries, versioning, artifact management, promotion and rollback.
• Model Monitoring & Drift Detection: monitor data quality, drift, performance, latency, and metrics.
• Feedback Loops & Continuous Training: capture predictions and ground-truth, evaluate and retrain with validation/approval processes.
• ML Pipeline Orchestration: develop scalable training, inference and retraining pipelines with Airflow, Kubeflow or cloud-native tools.
• Model Deployment & CI/CD: automated CI/CD/CT, serving APIs, containerized deployments, testing, release, rollback.
• Cloud ML Engineering: production workloads with AWS SageMaker, Azure ML, GCP Vertex AI or equivalents.
• Production Reliability & Governance: reproducibility, lineage, logging, alerts, observability, security, governance.

Skills

Python

SQL

ML fundamentals

Production ML pipelines

MLflow

Model monitoring

CI/CD

Git

Docker

Cloud platforms (AWS/Azure/GCP)

Deployment & troubleshooting

Education

Bachelor's or Master's in Engineering/CS or related field

Tools

MLflow

Airflow

Kubeflow

Docker

Kubernetes

Databricks

PySpark

Terraform

Feast

Job description

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.

Role: MLOps Engineer

Experience: 5-8 Years

Location: Bangalore/Pune/Noida/Jaipur

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

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.
• Bachelors or masters degree in Engineering, Computer Science or a related field.
• Strong problem-solving, troubleshooting and technical communication skills.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.