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Data Engineer/Machine Learning

2 months ago

Bethlehem, United States PRI Technology Full time

Requirements:

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.