Machine Learning Engineer

Transflo is seeking an experienced Machine Learning Engineer specializing in AWS Bedrock, MLflow, and advanced prompt engineering methodologies. The role involves leading the development of state-of-the-art MultiModal Document Identification and Extraction solutions, designing and fine-tuning foundation models, and implementing Generative AI strategies for efficient multimodal document processing.

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

Requirements:

• 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
• 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