Senior ML Engineer

4 months ago

Mumbai, India Magna Hire Full time

Primary Responsibilities :

- Deploying Machine learning and deep learning models developed by data science teams into production environments, ensuring they are scalable, robust and secure.


- Monitoring model performance and health in production, applying updates, and troubleshooting issues to minimize downtime and maintain service quality.


Secondary Responsibilities :

- Assisting in the development and optimization of spark jobs critical for data processing and model training tasks ensuring high performance and efficiency and providing guidance on best practices for large-scale data processing.


- Implementing and managing Apache Airflow to automate workflows, which includes setting up DAGs to orchestrate the machine learning pipelines. Ensuring that the workflows are efficient, reliable and timely and providing support for any issues related to workflow management.


- Ideally, should have worked closely with data scientists and machine learning engineers to understand their infrastructure needs and provide efficient solutions


Key Success Metrics :


- Model Uptime and Reliability, Model Performance in production, Efficiency of Spark Jobs, Workflow Automation Efficiency, User Satisfaction and Internal Feedback


- Managerial and Leadership Responsibilities : Leading a team of 4 Data scientists and ML engineers to deliver on primary and secondary :


- Familiarity with cloud platforms (AWS, Azure, GCP) and experience in managing and optimizing resources for training deep learning models.


- Experience with containerization and orchestration tools like Docker.


- Understanding of parallel and distributed computing principles.


- Experience with distributed training of deep learning models can be beneficial.


- Familiarity with distributed computing and big data technologies like Hadoop and Spark.


- Familiarity with machine learning operations (MLOps) principles and tools for managing the ML lifecycle.


- Should have worked on the infrastructure side of multiple machine learning projects, ideally involving deep learning models such as BERT and Transformers.


- Experience in managing resources and optimizing performance for large-scale machine learning tasks.


(ref:hirist.tech)

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