-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 such as docker or Kubernetes.
-3+ years of experience in developing and maintaining robust data pipelines data to be used by Data Scientists to build Client 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 such as Keras or PyTorch and libraries such as 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.
Pluses only:
-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.