Role Overview:
We are seeking an experienced Machine Learning Engineer specializing in AWS Bedrock, MLflow, and advanced prompt engineering methodologies to lead the development of state-of-the-art MultiModal Document Identification and Extraction solutions. In this role, you will design and fine-tune foundation models (FMs), implement Generative AI (GenAI) strategies, and leverage advanced prompt engineering techniques for accurate and efficient multimodal document processing.
Job Responsibilities:
Design, develop, and deploy scalable machine learning models using AWS Bedrock and SageMaker.
Implement and optimize multimodal machine learning pipelines for document identification and extraction.
Develop and refine advanced prompt engineering strategies, including hierarchical prompting, context-aware prompts, and multi-turn dialogue techniques, to enhance the performance of foundation models.
Manage the end-to-end ML lifecycle, including experiment tracking, model versioning, and deployment using MLflow.
Ensure robust MLOps practices, including CI/CD pipelines, model monitoring, and automated retraining workflows.
Optimize model inference performance and cost-effectiveness using AWS Elastic Inference and SageMaker optimization techniques.
Integrate AWS Textract and Rekognition for enhanced OCR and image processing within ML workflows.
Collaborate with cross-functional teams, including data scientists, cloud engineers, and business stakeholders, to align AI models with business objectives.
Monitor, debug, and enhance machine learning workflows for improved reliability and efficiency.
Stay updated on the latest advancements in AI, multimodal machine learning, and AWS technologies, and apply them to real-world problems.
Qualifications and Experience:
Extensive experience with AWS Bedrock for deploying and fine-tuning foundation models (FMs) for multimodal applications.
Proficiency in Amazon SageMaker for training complex ML models, hyperparameter tuning, and scalable deployment.
Hands-on experience with MLflow in AWS for experiment tracking, model versioning, and end-to-end ML lifecycle management.
Experience with AWS Lambda, API Gateway, and Step Functions for building serverless AI pipelines.
Familiarity with AWS Textract and Amazon Rekognition for document extraction and image recognition tasks.
Proficient in the utilization of Textual Models for Image Classification or other Open Source Image Classification tools.
Proficiency in AWS Deep Learning AMIs for rapid ML environment setup.
Experience with Amazon Elastic Inference for cost-effective inference acceleration
Image-Text Alignment Prompts – Creating prompts that effectively link textual and visual data for accurate information extraction.
Hierarchical Prompting – Designing prompts for complex document structures with nested elements.
Context-Aware Prompting – Developing prompts that adapt to the semantic context of documents.
Visual Layout-Aware Prompting – Crafting prompts that leverage document layout information for precise entity recognition.
Few-shot and Zero-shot Prompting – Utilizing examples to improve multimodal model performance with minimal labeled data.
Multi-turn Dialogue Prompting – Implementing iterative prompts for complex document extraction scenarios.
Cross-Attention Prompts – Optimizing attention mechanisms for aligning visual and textual features.
Individual Qualities:
Results oriented
Independently reliable; performs tasks without close supervision
Persistent Learner showing a desire to be on the edge of new AI methodologies as it may relate to current business opportunities.
Organized; detail-oriented, methodical and consistently demonstrates ability to successfully and timely complete assignments.
Follows-Up; consistently performs this in a positive, proactive manner
Logical problem-solving skills
Quality conscious; consistently demonstrates commitment to customers & quality
Demonstrates timeliness & urgency
Team work; individual contributor that works well with other team members and consistently promotes a strong team environment work ethic
Goal setting; sets/achieves goals and consistently demonstrates a willingness/dedication to process improvement
Responsible; takes responsibility for personal actions and consistently demonstrates a willingness to accept greater project responsibilities
Professionally candid communications
Focused on key success factors
Professional attitude; consistently demonstrates ability to accept criticism and manage the conversation appropriately
Street smart; can apply knowledge and life experiences in business
Positive attitude
Flexible & adaptable
Resourceful