ML Engineer

Description
As an ML Engineer, your role will be participating in field service 
accelerator development. The Field Service Engagement Accelerator is AI-driven solution designed to enhance customer interactions and service delivery in field service operations. This  accelerator leverages the latest advancements in machine learning, data processing, and cloud infrastructure to provide a customizable, scalable platform that can be rapidly deployed across multiple customers. This is greenfield project.

Key responsibilities include:
-Template Pipeline Development: Design and implement a flexible template 
pipeline that can be customized and deployed per customer. This pipeline will manage user input, integrating with LLMs using AWS Textract, AWS Connect, and agentic workflows.
-Building Data APIs: Develop robust APIs that interface with user engagement channels, capturing and processing inputs via AWS Textract and AWS Connect.
-Ingestion Pipeline Creation: Design and implement metadata-driven template ingestion pipeline that automates the loading of public data both web and documents (word, pdfs). This pipeline will support the creation of a multimodal knowledge base, which will be critical for customer-specific deployments.
-MLOps Integration: The project includes a comprehensive MLOps framework to manage the end-to-end lifecycle of machine learning models. This includes continuous integration and delivery (CI/CD) pipelines for model training, versioning, deployment, and monitoring. Automated workflows ensure that models are kept up-to-date with the latest data and are optimized for performance in production environments.
-Real-Time Engagement: With AWS services such as API Gateway, Lambda, and WebSocket, the accelerator supports real-time updates and asynchronous streaming, enhancing user engagement and reducing the risk of abandonment during interactions.
-Advanced Monitoring and Analytics: Integrated with AWS CloudWatch, 
QuickSight, and other monitoring tools, the accelerator provides real-time insights into performance metrics, user interactions, and system health. This allows for continuous optimization of service delivery and rapid identification of any issues.
-RAG Workflows: Establish Retrieval-Augmented Generation (RAG) workflows that leverage knowledge bases built on Kendra or OpenSearch. This includes integrating various data sources, such as corporate documents, inspection checklists, and real-time external data feeds.
Scalable and Customizable Solutions: Ensure that both the template and 
ingestion pipelines are scalable, allowing for adjustments to meet specific customerneeds and environments. This involves setting up RAG workflows, knowledge bases using Kendra/OpenSearch, and seamless integration with customer data sources.
-End-to-End Workflow Automation: Automate the end-to-end process from user input to response generation, ensuring that the solution leverages AWS services likeBedrock Agents, CloudWatch, and QuickSight for real-time monitoring and analytics.
 

Skills
Must have:
-Data engineering background is a must.
-Metadata driven data processing workflows (must have for lead, nice to have for developer)
-ML engineering background is a must, experience building ML pipelines, 
packaging and optimizing models, fine-tuning, packaging, serving.
-Strong expertise in AWS data and ML services
-Strong experience building data pipelines with Python

Nice to have:
-Experience or at least non-commercial experience with LLMs and agents.
-Langchain or similar
-AWS Bedrock
-AWS Bedrock knowledge bases
-Textract experience or any OCR experience related to document processing