You will:

• Collaborate with data scientists and analysts to understand data requirements and translate them into scalable, high performant data pipeline solutions.

• Support data discovery & data preparation for model development. Perform detailed analysis of raw data sources by applying business context and collaborate with cross-functional teams to transform raw data into curated & certified data assets to be used for ML and BI use cases.

• Collaborate with data science and data engineering team to build scalable and reproducible machine learning pipelines for training and inference.

• Implement machine learning models into operations and processes via batch, streaming and API methods.

• Monitor and troubleshoot data pipeline performance, identifying and resolving bottlenecks and issues.

• Develop, test, and maintain robust tools, frameworks, and libraries that standardize and streamline the data & machine learning lifecycle.

• Contribute to developing and maintaining end-to-end MLOps lifecycle to automate machine learning solutions development and delivery.

• Implement robust monitoring framework for model performance.

• Collaborate with cross-functional teams of Data Science, Data Engineering, business units and various IT teams.

• Create and maintain effective documentation for project and practices ensuring transparency and effective team communication.

You Have:

• Bachelor’s or master’s degree with 5 years of experience in Computer Science, Data Science, Engineering, or a related field.

• 4 years of experience in working with Python, SQL, PySpark and bash scripts. Proficient in software development lifecycle and software engineering practices.

• 2 years of hands-on experience in using Databricks platform

• 3 years of hands-on experience in operationalizing Machine Learning solutions which are used in live production processes.

• 2 years of experience and proficiency in API development using FastAPI frameworks and familiarity with containerization technologies like docker or Kubernetes.

• 3 years of experience in developing and maintaining robust data pipelines data to be used by Data Scientists to build ML Models.

• 3 years of experience working with Cloud Data Warehousing (Redshift, Snowflake, Databricks SQL or equivalent) platforms and experience in working with distributed framework like Spark.

• Solid understanding of machine learning life cycle, data mining, and ETL techniques.

• Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn, xgboost).

• Hands-on experience in building and maintaining tools and libraries which have been used by multiple teams across organization.

• Proficient in understanding and incorporating software engineering principles in design & development process.

• Hands on experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Github, Bitbucket), Orchestration (Airflow, Prefect or equivalent)

• Excellent communication skills and ability to work and collaborate with cross functional teams across technology and business.

Good to have:

• Familiarity with deep learning frameworks and deploying deep learning models for production use cases.

• Familiarity in using GPU compute either for model training or inference.

• Understanding of Large language models (LLM) and MLOps lifecycle for operationalizing LLM models.