Client is looking for someone from Bay Area who can come onsite for once a week and has great experience with Production ready ML and Data experience.
Key Skills :Machine Learning , Data , Python , Spark , heavy volume data processing, data platform, data lake, big data, data warehouse, or equivalent.
Data / ML Engineer (Onshore- Tuesday's in office) - Job Description
Required Skills & Experience:
- Hands-on code mindset with deep understanding in technologies / skillset and an ability to understand larger picture.
- Sound knowledge to understand Architectural Patterns, best practices and Non-Functional Requirements
- Overall, 8-10 years of experience in heavy volume data processing, data platform, data lake, big data, data warehouse, or equivalent.
- 5+ years of experience with strong proficiency in Python and Spark (must-have).
- 3+ years of hands-on experience in ETL workflows using Spark and Python.
- 4+ years of experience with large-scale data loads, feature extraction, and data processing pipelines in different modes – near real time, batch, realtime.
- Solid understanding of data quality, data accuracy concepts and practices.
- 2+ years of solid experience in building and deploying ML models in a production setup. Ability to quickly adapt and take care of data preprocessing, feature engineering, model engineering as needed.
- 2+ years of experience working with Python deep learning libraries like any or more than one of these - PyTorch, Tensorflow, Keras or equivalent.
- Prior experience working with LLMs, transformers. Must be able to work through all phases of the model development as needed.
- Experience integrating with various data stores, including:
- SQL/NoSQL databases
- In-memory stores like Redis
- Data lakes (e.g., Delta Lake)
- Experience with Kafka streams, producers & consumers.
- Required: Experience with Databricks or similar data lake / data platform.
- Required: Java and Spring Boot experience with respect to data processing - near real time, batch based.
- Familiarity with notebook-based environments such as Jupyter Notebook.
- Adaptability: Must be open to learning new technologies and approaches.
- Initiative: Ability to take ownership of tasks, learn independently, and innovate.
- With technology landscape changing rapidly, ability and willingness to learn new technologies as needed and produce results on job.
Preferred Skills:
- Ability to pivot from conventional approaches and develop creative solutions.
