ML Engineer Job in Chennai at Vestas

Vestas is hiring for the role of ML Engineer!

Responsibilities of the Candidate:

• Develop, test, and deploy statistical and machine learning models for advanced analytics use cases in the service operations area 
• Support Data Scientists in feasibility studies and rapid experimentation to build ML prototypes to validate & assess the potential impact, usefulness, complexity, and benefits of new ideas 
• Develop, produce, and deploy scalable ML solutions with automated drift monitoring from approved ML prototypes 
• Responsible for packaging ML solutions for end-user consumption, including building API endpoints, web applications, and end-user interfaces 
• Collaborate with data engineers to build scalable data pipelines to support ML deployments 
• Share knowledge and be an active consultant to explain and showcase the potentials of data science-driven use cases and industrializing machine learning in Vestas Service 

Requirements:

• Bachelors’ or Master’s degree in Engineering / Applied Statistics, with overall professional work experience in data, analytics, or relevant domain for 2-6 years
• Work experience as ML Engineer for 2+ years involving analytics product development where advanced data analytics, mathematical/statistical modeling, and data science were major parts of your work and followed a continuous delivery & deployment model
• Significant hands-on experience in modeling, experiment tracking, building and deploying training & inference pipelines for machine learning models and statistical models
• Proven work experience in developing solutions to production AI/ML prototypes, establishing continuous validation & retraining strategies, and developing necessary API end-points & web applications
• Experience in designing and deploying scalable AI/ML solutions using standard MLOps practices following ML Governance principles, setting up production-grade back-end (e.g. production-grade code, CI/CD, etc.), establishing monitoring and alerts (e.g. system runtime, data validation, data/model drift, etc.)
• Strong foundation in machine learning techniques (supervised & unsupervised, time series, NLP), neural networks, and statistics/mathematics/probability theory