Job Description:

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.