Senior Machine Learning Engineer - Python

Job Description :

Were seeking a Senior Machine Learning Engineer to lead the development, deployment, and optimization of scalable ML models that drive real business impact.

Youll be part of a cross-functional team of engineers, data scientists, and product managers, working on problems that sit at the intersection of data, algorithms, and product innovation.

This role is ideal for someone who thrives on solving open-ended problems using machine learning and can convert experimental models into reliable, production-ready systems.

Key Responsibilities :

- Design, develop, and deploy end-to-end ML solutions for real-world business challenges.

- Work on data preprocessing, feature engineering, model training, hyperparameter tuning, and performance evaluation.

- Own the model lifecyclefrom experimentation and versioning to deployment and monitoring in production.

- Collaborate with engineering teams to build scalable data pipelines and robust model serving infrastructure.

- Apply deep understanding of ML algorithms (supervised, unsupervised, and deep learning) to drive innovation.

- Stay ahead of the curve by researching new algorithms, frameworks, and best practices.

- Conduct rigorous A/B testing and ensure model interpretability, fairness, and compliance where applicable.

- Mentor junior ML engineers and participate in code reviews and design discussions.

Must-Have Qualifications :

- 58 years of experience in ML Engineering, Data Science, or Applied AI roles.

- Strong proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.

- Deep understanding of statistical modeling, algorithm design, and evaluation metrics.

- Experience with model deployment tools (e.g., MLflow, SageMaker, Kubeflow) and REST APIs.

- Familiarity with distributed data processing (e.g., Spark, Dask) and cloud platforms (AWS, GCP, or Azure).

- Hands-on experience with CI/CD for ML workflows and production-grade codebases.

- Excellent communication skills and the ability to work cross-functionally.

Nice to Have :

- Experience with NLP, time-series forecasting, recommender systems, or computer vision models.

- Familiarity with MLOps, data versioning tools like DVC, and monitoring tools like Prometheus/Grafana.

- Background in mathematics, statistics, or a related quantitative field (M.S. or Ph.D. preferred).