Hiring: Data Scientist (Python | Machine Learning | GCP | MLOps)

Experience: 6–8 Years
Locations: Gurugram, Bengaluru, Pune, Jaipur, New Delhi, Noida, Hyderabad, Chennai, Bhopal, Mumbai
Work Mode: Hybrid

Job Overview

We are looking for an experienced Data Scientist with a strong engineering background and hands-on expertise in Python, Machine Learning, and MLOps.

The ideal candidate will be responsible for designing, developing, training, validating, and deploying machine learning models into production environments. The role requires practical experience with GCP, Vertex AI, Kubernetes, and Argo Workflows to build scalable, reliable, and production-ready ML pipelines.

Key Responsibilities

1. Machine Learning & Model Development

- Develop, train, and validate machine learning models using Python.
- Perform exploratory data analysis (EDA), feature engineering, and model evaluation.
- Apply machine learning techniques for prediction, classification, and optimization use cases.
- Work with ML frameworks such as PyTorch, Scikit-learn, and CatBoost.

2. ML Workflow Orchestration

- Design and implement ML workflows using Argo Workflows on Kubernetes.
- Orchestrate model inference and post-processing pipelines.
- Build automated and repeatable workflows for ML experiments and production inference.

3. Model Training & Validation

- Use Google Vertex AI for scalable model training and hyperparameter tuning.
- Implement model validation and evaluation strategies.
- Ensure reproducibility and consistency across training runs.

4. Model Deployment & Inference

- Develop and maintain Python-based runtimes for model training, inference, and feature engineering.
- Optimize inference pipelines for performance, reliability, and scalability.
- Monitor and manage ML pipelines in production environments.




- Participate in on-call rotations for inference infrastructure support.

5. Model Lifecycle Management

- Manage trained model artifacts using Google Cloud Storage (GCS).
- Maintain model metadata, versioning, and lineage using Argo or custom model registries.
- Support model rollback, version control, and auditability.

6. Monitoring & Governance

- Define model evaluation metrics and validation criteria.
- Implement post-deployment monitoring and drift detection.
- Support model retraining strategies and continuous model improvement.
- Follow best practices for documentation, testing, and responsible AI.

Mandatory Technical Skills

- 6–8 years of experience in Data Science / Machine Learning.
- Robust hands-on proficiency in Python.
- Practical experience with machine learning frameworks such as PyTorch, Scikit-learn, and CatBoost.
- Strong understanding of feature engineering, EDA, model training, and evaluation.
- Hands-on experience with Kubernetes and Kubernetes-based workflows.
- Experience with Argo Workflows.
- Exposure to Google Cloud Platform (GCP) and Vertex AI.
- Experience taking ML models from experimentation to production inference.
- Strong understanding of MLOps principles and ML lifecycle management.

Good to Have

- Experience with real-time and batch inference systems.
- Exposure to CI/CD for ML pipelines.
- Knowledge of model monitoring, drift detection, and automated retraining.
- Experience with production-grade ML infrastructure.

Soft Skills

- Strong analytical and problem-solving abilities.
- Ability to translate business requirements into data science solutions.
- Good communication and collaboration skills.
- Ability to work with cross-functional engineering and business teams.

Pay: ₹100,000.00 - ₹130,000.00 per month

Experience:

- Data Science: 6 years (Required)

Work Location: Hybrid remote in Noida, Uttar Pradesh

📌 Data Scientist – Python & MLOps (Noida)
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