Platform Engineer II

Job Summary

Experience in designing & implementing solution in mentioned areas:

Strong experience in developing AI/ML models on Google Cloud Platform Core Skills : TensorFlow, Recommendation Systems, Deep Learning, NumPy, Pandas, Sklearn, Python, R, Keras, Statistical Analysis, Computer Vision, PySpark

Key Responsibilities

Min 5 years of experience in Machine Learning, predictive analytics

• 5+ years of hands on experience in developing models using Machine Learning and Deep Learning related technologies such as Keras, TensorFlow, pyTorch, GCP AI/ML services.
• 5+ years of experience in Python, Spark. Experience working in python libraries like TensorFlow, Spark ML, scikit-learn, pandas, NumPy etc.
• Software engineering in Python, good with Python SDK, able to build libraries and comfortable in deploying Python codes in production.
• Hands on experience on Model deployment and Model Monitoring on any public cloud (AWS/Azure/ GCP). GCP experience is an added advantage
• Prior experience of handling Supervised Learning, Un-supervised learning, and Reinforcement learning problems in different industry verticals (Banking/ Finance/ telecom/Retail/ technology etc)
• Experience transforming data science prototypes into robust, scalable products running seamlessly in production
• Experience with implementing CI/CD principles in the Machine Learning domain (ML Ops)
• Exposer to containers and its orchestration (Kubernetes).
• Google Cloud Platform: BigQuery, Cloud Composer, Vertex AI & AI/ML services in general
• Strong hold of concepts in Statistics and expertise in Machine Logs processing, text mining and text analytics.

Skill Requirements

Technical / Functional Skills:- Experience in designing & implementing solution in mentioned areas:

Strong experience in developing AI/ML models on Google Cloud Platform Core Skills : TensorFlow, Recommendation Systems, Deep Learning, NumPy, Pandas, Sklearn, Python, R, Keras, Statistical Analysis, Computer Vision, PySpark

Other Requirements

1. Certification in Semiconductor Device Characterization or related fields is optional but valuable (e.g., IEEE Certified Semiconductor Professional).