Job Description & How to Apply Below
Experience:
6–8 years
Role Overview
We are seeking a Data Scientist with a strong engineering background to design, build, train, and operationalize
machine learning models that deliver measurable business impact.
You will work end-to-end across feature engineering, model training, inference, and post-processing, leveraging a
modern, cloud-native ML platform built on GCP and Kubernetes.
This role blends strong statistical and machine learning expertise with hands-on MLOps practices, ensuring
models are reliable, scalable, and production-ready.
Key Responsibilities
Model Development & Data Science
● Develop, train, and validate machine learning models using Python.
● Perform feature engineering, exploratory data analysis, and model evaluation.
● Apply appropriate ML techniques for prediction, classification, or optimization use cases.
ML Workflow Orchestration
● Use Argo Workflows on Kubernetes to orchestrate model inference and post-processing pipelines.
● Design repeatable, automated workflows for ML experiments and production inference.
Model Training & Validation
● Leverage Vertex AI to run scalable model training, hyperparameter tuning, and validation.
● Ensure reproducibility and consistency across training runs.
Model Runtime & Inference
● Build and maintain Python-based runtimes for training, inference, and feature engineering.
● Optimize inference pipelines for performance, reliability, and scalability.
● Handle and monitor pipelines in production environments..
● Participate in Oncall rotation for inference infrastructure.
Model Storage & Lifecycle Management
● Handle trained artifacts in Google Cloud Storage (GCS).
● Track model metadata, versions, and lineage using Argo or custom model registries.
● Support model versioning, rollback, and auditability..
Quality, Monitoring & Governance
● Define model evaluation metrics and validation criteria.
● Support post-deployment monitoring, drift detection, and retraining strategies.
● Follow best practices for documentation, testing, and responsible AI usage.
Required
Skills & Qualifications
Technical Skills
● Strong on-hands proficiency in Python for data science and machine learning use-cases.
● Hands-on experience with ML frameworks (e.g., PyTorch, scikit-learn, catboost, …).
● Familiarity with Kubernetes and Kubernetes-based workflows, especially Argo Workflows.
Data Science & ML Concepts
● Strong understanding of feature engineering, model evaluation, and validation techniques.
● Experience taking models from experimentation to production inference.
● Understanding of ML lifecycle management and MLOps principles.
Soft Skills
● Strong analytical and problem-solving mindset.
● Ability to translate business problems into data science solutions.
● Clear communication skills to explain models and results to diverse stakeholders.
Nice to Have
● Experience with real-time or batch inference systems.
● Exposure to CI/CD for ML pipelines.
● Familiarity with model monitoring, drift detection, and retraining strategies
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