Senior Machine Learning Engineer - Python/Tensorflow (8-12 yrs)

Required Skills :

- Master's degree in Computer Science, Computational Sciences, Data Science, Machine Learning, Statistics , Mathematics any quantitative field

- Expertise with object oriented programming (Python, C++)

- Strong expertise in Python libraries like NumPy, Pandas, PyTorch, TensorFlow, and Scikit-learn

- Proven experience in designing and deploying ML systems on cloud platforms (AWS, GCP, or Azure).

- Hands-on experience with MLOps frameworks, model deployment pipelines, and model monitoring tools.

- Track record of scaling machine learning solutions from prototype to production.

- Experience building scalable ML systems in fast-paced, collaborative environments.

- Working knowledge of adversarial machine learning techniques and their mitigation

- Agile and Waterfall methodologies.

- Personally invested in continuous improvement and innovation.

- Motivated, self-directed individual that works well with minimal supervision

- Familiarity with Snowpark ML and Snowflake Cortex.

- Experience with Azure ML & Azure ML Studio.

Role :

- This is a global role working across diverse business areas, brand and geographies, providing business outcomes and enabling transformative impact across the global landscape.

- Design and deploy end-to-end machine learning pipelines on cloud platforms (Azure, Snowflake) to deliver scalable, production-ready solutions.

- Build efficient ETL pipelines to support data preparation, model training, and evaluation on modern platforms like Snowflake.

- Scale machine learning infrastructure to handle large datasets and enable real-time processing for critical applications.

- Implement MLOps frameworks to automate model deployment, monitoring, and retraining, ensuring seamless integration of ML solutions into business workflows.

- Monitor and measure model drift (concept, data, and performance drift) to maintain ongoing model effectiveness.

- Deploy machine learning models as REST APIs using frameworks such as FastAPI, Bento ML, or Torch Serve.

- Establish robust CI/CD pipelines for machine learning workflows using tools like Git and Jenkins, enabling efficient and repeatable deployments.

- Ensure secure ML deployments, addressing risks such as adversarial attacks and maintaining model integrity.

- Build modular and reusable ML packages using object-oriented programming principles, promoting code reusability and efficiency.

- Develop clean, efficient, and production-ready code by translating complex business logic into software solutions.

- Continuously explore and evaluate MLOps tools such as MLFlow and Weights & Biases, integrating best practices into the development process.

- Foster cross-functional collaboration by partnering with product teams, data engineers, and other stakeholders to align ML solutions with business objectives.

- Lead data labeling and preprocessing tasks to prepare high-quality datasets for training and evaluation.

- Stay updated on advancements in machine learning, cloud platforms, and secure deployment strategies to drive innovation in ML infrastructure.