Machine Learning Engineer - AWS Platform

<p><p>Job Role : ML Engineer.</p><p><br/>Experience : 6-12 Years.<br/><br/>Location : Pune, Bangalore, Hyderabad, Trivandrum, Chennai, Kochi, Gurgaon, Noida.</p><p><br/></p><p><b>Key Summary :</b></p><p><br/></p><p>The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and Development :</b></p><p><br/></p><p>- Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern Preparation :</b></p><p><br/></p><p>- Prepare, cleanse, and transform data for model training and Implementation :</b></p><p><br/></p><p>- Implement and optimize machine learning algorithms and statistical Integration :</b></p><p><br/></p><p>- Integrate models into existing systems and Deployment :</b></p><p><br/></p><p>Deploy models to production environments and monitor :</b></p><p><br/></p><p>- Work closely with data scientists, software engineers, and other Improvement :</b></p><p><br/></p><p>- Identify areas for improvement in model performance and :</b></p><p><br/></p><p>- Programming and Software Engineering : Knowledge of software engineering best practices (version control, testing, CI/CD).<br/><br/>- Data Engineering : Ability to handle data pipelines, data cleaning, and feature engineering.<br/><br/>- Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph.<br/><br/>- Model Deployment and Monitoring : MLOps Experience in deploying ML models to production environments.<br/><br/>- Knowledge of model monitoring and performance experience :</b></p><p><br/></p><p>- Amazon SageMaker : Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements.<br/><br/>- AWS Cloud Infrastructure : Familiarity with S3, EC2, Lambda and using these services in ML workflows.<br/><br/>- AWS data : Redshift, Glue.<br/><br/>- Containerization and Orchestration : Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS).</p><p><b><br/></b></p><p><b>Skills :</b></p><p><br/></p><p>- Aws, Aws Cloud, Amazon Redshift, Eks.</p><br/></p> (ref:hirist.tech)