5.0 - 10.0 years

10 - 20 Lacs

Bengaluru

Work from Office

Must have: 5+ years of experience in designing, developing, and deploying AI/ML solutions, with at least 3+ years focused on AWS AI/ML services. Deep hands-on experience with Amazon SageMaker for building, training, tuning, and deploying ML models. Proven ability to work with AWS data services, such as: Amazon S3 (data storage),AWS Glue or AWS Data Wrangler (data processing), Amazon Athena or Redshift (querying/analytics) Familiarity with AWS AI services, like:Amazon Rekognition (computer vision), Amazon Comprehend (NLP), Amazon Transcribe/Polly (speech), Amazon Lex (chatbots) Experience building end-to-end ML pipelines using AWS-native tools or integrating with tools like Step Functions, Lambda, and CloudWatch for automation and monitoring. Solid understanding of model versioning, deployment strategies (real-time, batch, A/B testing), and model monitoring on AWS. Proficiency in Python for ML model development and deployment. Good to have: Hands-on experience with MLOps practices using AWS tools (e.g., SageMaker Pipelines, Model Registry, CodePipeline, CloudFormation). Familiarity with data lake architecture and tools like AWS Lake Formation. AWS certifications (e.g., AWS Certified Machine Learning Specialty, Solutions Architect – Associate/Professional).Experience with Application performance tuning

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