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Tier-1 brand, mid-level ML role, metro context, and broad AWS/MLOps requirements increase applicant competition.
Transferable ML skills, but AWS SageMaker and telco Big Data specifics increase domain dependency.
Mandatory 3+ years, specific AWS SageMaker stack, programming languages, and certifications raise filtering strictness.
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Design, develop, and productionise machine learning systems and applications using AWS services.
Automate predictive model software, including model training and end-to-end ML pipelines with MLOps.
Collaborate with architecture teams to evolve Big Data platform capabilities and improve data pipelines for ML models.
3+ years of experience as an AI/ML Engineer and 3+ years in BI or related software development roles.
Mandatory hands-on experience with AWS services: SageMaker Pipelines, SageMaker Studio, CloudFormation, CloudTrail, SNS, EventBridge, CodePipeline, CodeBuild, CodeCommit.
Strong programming skills in Java and Python with experience in machine learning frameworks like TensorFlow or H2O.
3-year degree or diploma in IT/IS or related field is essential; relevant cloud certification at professional or associate level is required.
Experienced in managing agile development lifecycles (Kanban/Scrum) and building end-to-end ML production systems.
Strong background in data modelling, data architecture, and Big Data technologies including Hadoop, Spark, Hive, Yarn, and Airflow.
Hands-on expertise in containerisation (Docker/Kubernetes) and evolving scalable ML applications with reusable patterns on AWS.