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High due to Tier-1 brand, mid-level ML role, and Pune metro location.
Medium because ML/MLOps skills transfer across industries, though AWS SageMaker and telecom context add some domain specificity.
High because of mandatory 5+ years, specific AWS SageMaker/MLOps stack, Java/Python and certifications.
Design, develop, and productionise machine learning systems and applications using AWS services tailored for local markets and group functions.
Automate and maintain predictive model software, including managing data flow, data quality, and creating end-to-end ML pipelines with MLOps practices.
Collaborate with architecture teams to evolve Big Data platform capabilities through reusable components, and research new technologies to improve ML application sustainability and delivery.
5+ years of experience as AI/ML Engineer and 5+ years in BI or related software development.
Mandatory hands-on experience with AWS services: SageMaker Pipelines, SageMaker Studio, CloudFormation, CloudTrail, SNS, EventBridge, CodePipeline, CodeBuild, CodeCommit.
3-year IT or IS degree or diploma (or related field) is essential; advanced degree and relevant cloud certification at professional or associate level preferred.
Proficient programming skills in Java and Python with experience in distributed ML frameworks (e.g., TensorFlow, H2O) and Big Data frameworks (Hadoop, Spark, Hive).
Experienced in managing agile software development lifecycles (Kanban or Scrum) in data science or ML engineering contexts.
Strong background in building and scaling production-grade ML systems with robust data modelling, MLOps, and AWS-native pipeline automation.
Comfortable collaborating with architecture teams to design reusable Big Data assets and optimizing data pipelines for efficiency and accuracy.