Machine Learning Engineer

Job Description:

• Strong experience in Python.
• 3+ years experience of working on feature/data pipelines using Py-Spark and feature stores.
• Exposure to AWS cloud services such as Sagemaker, Bedrock, Kendra etc.
• Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practice.
• Demonstrable ability to think outside of the box and not be dependent on readily available tools.

Py-Spark AWS Engineer:

• Good hands-on experience of python and Bash Scripts
• 3-5 years of good hands-on exposure with Big Data technologies Pyspark (Data frame and Spark SQL), Hadoop, and Hive
• Hands-on experience with using Cloud Platform provided Big Data technologies (i.e. Glue, EMR, RedShift, S3)
• Good understanding of SQL and data warehouse tools like (Redshift)
• Strong analytical, problem-solving, data analysis and research skills.
• Demonstrable ability to think outside of the box and not be dependent on readily available tools.

Roles & Responsibilities

• Ability to understand business requirements.
• Write clean, efficient, and reusable code following best practices.
• Troubleshoot and debug applications to ensure optimal performance.
• Write unit test cases.
• Generate actionable insights for business improvements.
• Actively drive a culture of knowledge-building and sharing within the team

Good To Have:

ML Engineer:

• Experience researching and applying large language and Generative AI models.
• Experience with Langchain, LLAMA Index, and Performance Evaluation frameworks.
• Knowledge on Docker and Kubernetes.

Py-Spark AWS Engineer:

• Orchestration with Airflow and Any job scheduler experience
• Experience in migrating workload from on-premises to cloud and cloud to cloud migrations.

Mandatory Skills

• Pyspark, AWS, AI/ML