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Machine Learning Engineer
2 months ago
Experience: 5-8 Years
Responsibilities
- Work closely with MLEs, MLOps, Data Engineers, and Data Scientists to build and extend our DS/ML platform and use cases.
- Build MVP applications to showcase the value of DS/ML models, owning the end-to-end process.
- Develop re-usable production-grade functionality and automation around DS/ML feature pipelines, real-time/batch inference, continuous training, model monitoring, and more.
- Participate in design sessions and brainstorming with the team on how to solve complex data problems with scalable solutions.
- Work closely with Data Scientists to support productionizing their use case.
- Use Python, Spark, AWS, Snowflake.
- Perform code review and end-to-end testing.
- Can demonstrate awareness of data visualization tooling and techniques.
Ideal Candidate will
- Decent understanding of Machine Learning Systems (model training, model inference, APIs, feature pipelines, continuous training, model monitoring).
- Have 5 to 8 years of experience as data scientist, data analyst with an appropriate background in ML, DL or statistical modeling.
- Have experience and awareness of applying popular analytical and statistical techniques.
- Have knowledge of serving a model and its pipelines in production.
- Be familiar with a machine learning framework : PyTorch (preferred), TensorFlow, or Keras.
- Understand concepts of linear algebra and/or statistics where applicable.
- Understand the patterns, anti-patterns, pitfalls of all important techniques outlined in the Responsibilities.
- Good proficiency in the language of their choice.
- Have excellent communication skills and can work in a team
