Role Overview :

The role requires the ability to independently drive ML initiatives from problem definition and model development through deployment, monitoring, optimization, and continuous improvement. The candidate will also establish reusable engineering practices and collaborate with cross-functional teams to deliver reliable, production-ready solutions.

Key Responsibilities :

- Own the end-to-end lifecycle of ML solutions, including problem formulation, data preparation, feature engineering, model development, validation, deployment, and monitoring.

- Develop and optimize models for classification, regression, forecasting, anomaly detection, and business-specific use cases.

- Design modular, reusable, and scalable ML pipelines supporting automated training, batch scoring, and real-time inference.

- Build production-quality Python applications using sound software design principles, object-oriented programming, modular architecture, and robust error handling.

- Develop automated workflows using Airflow, MLflow, or equivalent platforms for orchestration, experiment tracking, model registration, and reproducibility.

- Establish and implement MLOps practices covering automated testing, CI/CD, deployment validation, model versioning, release management, and rollback mechanisms.

- Integrate ML models with enterprise applications, REST APIs, databases, feature stores, and data platforms.

- Implement model monitoring for performance degradation, data drift, model drift, inference latency, data quality, and system reliability.

- Automate model retraining, validation, and deployment workflows where appropriate, incorporating suitable approval and governance controls.

- Optimize pipeline execution time, memory utilization, parallel processing, and infrastructure costs for high-volume workloads.

- Collaborate with data engineering, DevOps, cloud, and application engineering teams to operationalize ML solutions on cloud and enterprise platforms.

- Lead technical troubleshooting and root-cause analysis for complex model, data, and pipeline issues.

- Conduct code reviews, mentor junior data scientists, and promote standards for testing, documentation, reproducibility, and maintainability.

- Ensure adherence to security, explainability, responsible AI, and model governance requirements.

Required Skills & Expertise :

- Strong proficiency in Python, SQL, object-oriented programming, and production software engineering practices.

- Demonstrated experience building and deploying ML models in enterprise or production environments.

- Expertise in feature engineering, model evaluation, hyperparameter optimization, and model performance improvement.

- Experience designing automated ML pipelines spanning ingestion, transformation, training, inference, and monitoring.

- Hands-on experience with MLflow, Airflow, or equivalent tools for workflow orchestration and model lifecycle management.

- Experience implementing CI/CD, automated testing, Git-based version control, and deployment workflows.

- Strong understanding of batch and online inference patterns, API integration, logging, exception handling, and operational monitoring.

- Experience diagnosing data drift, model drift, performance degradation, and production pipeline failures.

- Knowledge of distributed processing, parallelization, resource optimization, and scalable data processing.

- Ability to independently manage deliverables, make technical decisions, and work effectively across teams.