Data/Machine Learning Engineer

Job Description

Greeting's from Skylark Information Technologies pvt ltd We have an opening for Data/Machine learning Engineer,if your interested please share your updated profile to the mentioned mail id which is hrr@skylarkinfo.com, Experience-2 to 4yrs Location -Chennai Notice Period-Immediate/20 days/30 days only JD Core Responsibilities: Design, build, and maintain scalable data pipelines and ETL processes using AWS services like Glue, EMR, Lambda, Step Functions, and Data Pipeline . Develop and optimize data lakes and data warehouses using Amazon S3, Redshift, and Athena for efficient querying and storage. Implement real-time and batch data processing using Kinesis, Kafka (MSK), and AWS Lambda . Build and deploy machine learning models using SageMaker, Lambda, and Step Functions for inference and automation. Develop feature engineering pipelines and ensure proper data preprocessing using AWS Glue, Pandas, and PySpark . Work with AWS AI/ML services (e.g., SageMaker, Rekognition, Comprehend, Polly) for NLP, image processing, and deep learning workloads. Ensure data security and governance by implementing IAM roles, KMS encryption, AWS Lake Formation, and AWS Glue Catalog . Optimize storage, indexing, and retrieval strategies for structured and unstructured data using DynamoDB, S3 Glacier, and OpenSearch . Automate MLOps using SageMaker Pipelines, CI/CD with AWS CodeBuild, and Model Registry for ML lifecycle management. Build scalable API-based data solutions using API Gateway, Lambda, and AWS Fargate for seamless model serving. Performance & Optimization: Optimize query performance in Athena, Redshift Spectrum, and Glue using partitioning, bucketing, and compression techniques. Monitor data pipeline performance using AWS CloudWatch, AWS X-Ray, and AWS Step Functions Execution History . Implement cost optimization strategies , such as spot instances for EMR jobs, S3 lifecycle policies, and instance right-sizing for compute resources. Utilize AWS Auto Scaling and serverless architectures (Lambda, Fargate) for efficient workload scaling. Collaboration & Best Practices: Work closely with Data Scientists, Data Analysts, and DevOps Engineers to align infrastructure and ML workflows. Ensure high availability and fault tolerance of data pipelines with multi-AZ, backup strategies, and disaster recovery . Implement version control and reproducibility for ML models and data pipelines using AWS CodeCommit, DVC, or GitHub Actions . Develop and enforce data quality checks using AWS Deequ, Great Expectations, or custom validation frameworks . Stay updated with AWS advancements in data and ML engineering, including new AI/ML services, database innovations, and serverless trends . Drive compliance and regulatory best practices (GDPR, HIPAA, SOC 2) for data storage, processing, and model deployment. Thank you, -Revathy k Sr.HR Skylark Information Technologies Pvt ltd