Description

•    8+ years of hands-on experience in Data Science and Data Engineering, with a proven track record of designing and deploying enterprise-scale analytical solutions. 
•    Expert-level proficiency in Python, alongside deep knowledge of Architectural Patterns for distributed computing and large-scale data processing (a must-have requirement). 
•    4+ years of dedicated experience in MLOps and Productionalization, focusing on the end-to-end lifecycle of machine learning models (CI/CD for ML). 
•    Strong understanding of Data Architecture, and real-time streaming architectures (e.g., Kafka or Spark Streaming). 
•    Experience with Cloud Infrastructure (AWS/Azure/GCP) and containerization (Docker/Kubernetes) to build scalable, resilient environment for model training and inference. 
•    Excellent system design and analytical skills, with a focus on model governance, security, and scalability of AI solutions. 
•    Superior communication and leadership skills, with the ability to align technical roadmaps with business strategy and explain architectural trade-offs to executive stakeholders. 
•    Proven ability to lead cross-functional teams, define technical standards, and drive the adoption of innovative data technologies in a fast-paced environment

Key Skills

Education

Bachelor's degree