S enior Data Scientist ML & MLOps

Experience: 6+ Years

Locations: Gurugram | Pune | Bengaluru | Hyderabad | Chennai | Bhopal | Jaipur

Work Mode: Hybrid 2–3 Days from Office

Shift: 12:00 PM – 9:00 PM IST

Role Overview

We are looking for a Senior Data Scientist with a strong engineering and machine learning background to build, deploy, and operationalize production-grade ML solutions.

The role involves the complete ML lifecycle — from data exploration, feature engineering, model development and training to production inference, monitoring, and model lifecycle management .

You will work with a modern GCP and Kubernetes-based ML platform , using technologies such as Python, Vertex AI, Kubernetes, and Argo Workflows .

Key Responsibilities

Data Science & Model Development

- Develop, train, validate, and optimize machine learning models using Python .
- Perform EDA, feature engineering, model selection, and performance evaluation .
- Apply appropriate ML techniques for prediction, classification, optimization, and other business use cases.
- Work with ML frameworks such as scikit-learn, PyTorch, CatBoost , or similar.

ML Pipeline & Workflow Orchestration

- Design and implement automated ML workflows using Argo Workflows on Kubernetes .
- Build repeatable pipelines for model training, inference, and post-processing.
- Ensure ML workflows are scalable, reliable, and production-ready.

Model Training & Inference

- Use Google Vertex AI for scalable model training, validation, and hyperparameter tuning.
- Develop and maintain Python-based training, inference, and feature-engineering runtimes .
- Optimize inference pipelines for performance, scalability, and reliability.
- Support both batch and real-time inference use cases.




- Monitor and troubleshoot production ML pipelines.
- Participate in on-call support for inference infrastructure.

Model Lifecycle & Storage

- Manage trained model artifacts using Google Cloud Storage (GCS) .
- Maintain model versions, metadata, lineage, and related artifacts.
- Support model versioning, rollback, reproducibility, and auditability .
- Work with model registries or custom model-management solutions.

Monitoring, Quality & Governance

- Define appropriate model evaluation metrics and validation criteria .
- Support production monitoring, model drift detection, performance monitoring, and retraining .
- Implement best practices around ML testing, documentation, reproducibility, and responsible AI.
- Collaborate with engineering and platform teams to ensure reliable ML operations.

Required Skills Must-Have

- 6+ years of hands-on experience in Data Science / Machine Learning.
- Robust programming skills in Python .
- Strong understanding of machine learning, statistics, feature engineering, model evaluation, and validation .
- Hands-on experience with ML frameworks such as:
- scikit-learn
- PyTorch
- CatBoost or similar
- Experience taking ML models from experimentation to production .
- Practical understanding of MLOps and ML lifecycle management .
- Hands-on exposure to Kubernetes .
- Experience with Argo Workflows or Kubernetes-based workflow orchestration.
- Experience with Google Cloud Platform (GCP) and preferably Vertex AI .

Good to Have

- Experience with real-time or batch inference systems .
- Experience with ML CI/CD pipelines .
- Knowledge of model monitoring and drift detection .
- Experience implementing automated model retraining strategies.
- Exposure to model registries, metadata management, and model lineage.

📌 Data Scientist (Gurugram)
🏢 Technocratic Solutions
📍 Gurugram