Machine Learning Engineer - BLR GGN HYD

Location: Bangalore, HYD or GGN - RTO

Inviting applications for the role of a ML Engineer - Data Science and Insights.

An ML engineer position will require either a bachelor's degree in computer science engineering, information technology or BSc in Computer Science, Mathematics or similar field; Masters degree is a plus.

Basic Qualifications

• Professional software development experience
• Programming experience with languages such as python, Java and R
• Experience in machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn) etc.
• Deep knowledge of math, probability, statistics and algorithms
• Understanding of data structures, data modeling and software architecture

Description

• Designing and developing machine learning and deep learning systems
• Running machine learning tests and experiments
• Implementing appropriate ML algorithms

ML Engineer Data Science and Insights Key Skills Find below the skills under various categories on which a ML Engineer should have experience on following

• Integration APIs, micro-services and ETL/ELT patterns
• DevOps (Good to have) – Ansible, Jenkins, ELK
• Containerization – Docker, Kubernetes etc
• Orchestration – Airflow, Step Functions, Ctrl M etc.
• Languages and scripting: Python, Scala Java etc
• Cloud Services - AWS, GCP, Azure and Cloud Native
• Analytics and ML tooling – Sagemaker, ML Studio
• Execution Paradigm – low latency/Streaming, batch and micro batch processing
• Data platforms – Big Data (Hadoop, Spark, Hive, Kafka etc.) and Data Warehouse (Teradata, Redshift, BigQuery, Snowflake etc.)
• Visualization Tools - PowerBI, Tableau

Roles and Responsibilities

• Understanding of data structures, data modeling and software architecture.
• Deep knowledge of math, probability, statistics and algorithms.
• Drive the vision for modern data and analytics platform to deliver well architected and engineered data and analytics products leveraging cloud tech stack and third-party products.
• Close the gap between ML research and production to create ground-breaking new products, features and solve problems for our customers.
• Design, develop, test, and deploy data pipelines, machine learning infrastructure and client-facing products and services.
• Build and implement machine learning models and prototype solutions for proof-of concept.
• Scale existing ML models into production on a variety of cloud platforms.
• Analyze and resolve architectural problems, working closely with engineering, data science and operations teams