Machine Learning Engineer

Role overview Develop and operate machine learning solutions that turn varied customer information into useful, monitored AI workflows. The role covers the model lifecycle, from adapting pretrained models and building retrieval augmented generation systems to automating deployment, updates, and production analytics on AWS.

Responsibilities - Fine tune pretrained models such as BERT or GPT for specific tasks and deploy them through SageMaker or Bedrock. - Build RAG workflows and knowledge bases using Kendra or OpenSearch. - Connect customer data sources, including documents, inspection checklists, and live external feeds. - Create MLOps processes for training, versioning, deployment, and ongoing model monitoring. - Automate the flow from user input to generated response, using services such as Bedrock Agents. - Track performance, user interactions, and system health with CloudWatch, QuickSight, and related monitoring tools. - Design ingestion pipelines and templates that can scale and adapt to different customer environments.

Requirements - Practical experience fine tuning and deploying machine learning models. - Experience building RAG systems and integrating knowledge sources. - Familiarity with model lifecycle automation, including training, versioning, delivery, and monitoring. - Ability to build scalable data ingestion and application workflows on AWS. - Understanding of production monitoring and analytics for AI services.

Nice to have - Experience with BERT, GPT, SageMaker, Bedrock, Kendra, OpenSearch, Bedrock Agents, CloudWatch, or QuickSight.