Senior AI/ML Engineer

Transflo is a leading provider of mobile, telematics, and business process automation software for the transportation and logistics industry. They are seeking a Senior AI/ML Engineer to lead the design and development of their Intelligent Document Processing platform, focusing on applying AI and ML solutions to improve document handling in the logistics sector.

Responsibilities:

• Design and build end-to-end AI systems for intelligent document processing, combining large language models (LLMs), vision-language models (VLMs), and classical ML techniques to solve document classification, entity extraction, and data validation challenges
• Architect multimodal AI pipelines that process structured, semi-structured, and unstructured documents containing mixed text, images, tables, handwriting, and complex layouts
• Evaluate, select, and deploy foundation models (FMs) via AWS Bedrock, including fine-tuning, retrieval-augmented generation (RAG), and model adaptation strategies appropriate to document intelligence use cases
• Develop and continuously refine advanced prompt engineering strategies — including hierarchical prompting, context-aware prompts, visual layout-aware prompts, few-shot and zero-shot techniques, multi-turn dialogue, image-text alignment prompts, and cross-attention optimization — to maximize accuracy and robustness of FM-based extraction pipelines
• Stay current on frontier AI research (multimodal transformers, document foundation models, agentic LLM patterns) and translate relevant advancements into production system improvements
• Design, train, and deploy scalable ML models using Amazon SageMaker, including experiment management, hyperparameter tuning, distributed training, and endpoint deployment
• Own the full ML lifecycle using MLflow on AWS: experiment tracking, model versioning, artifact management, model registry, and promotion workflows from experimentation to production
• Build and maintain robust MLOps infrastructure including CI/CD pipelines for model training and deployment, automated model monitoring, drift detection, and triggered retraining workflows
• Optimize model inference performance and cost-efficiency using Amazon Elastic Inference, SageMaker inference optimization features, model quantization, batching strategies, and caching patterns
• Implement evaluation frameworks and benchmark suites to rigorously measure model accuracy, extraction quality, latency, and regression risk across document types and edge cases
• Implement and optimize multimodal ML pipelines for document classification, field extraction, layout understanding, and semantic interpretation across diverse freight and logistics document types
• Integrate AWS Textract for OCR, form extraction, and table parsing; integrate Amazon Rekognition for image classification, object detection, and visual content analysis within AI workflows
• Apply textual models for image classification and leverage open-source vision-language tools (e.g., LLaVA, PaddleOCR, LayoutLM variants, Donut) to extend and complement AWS-native capabilities
• Design prompting and extraction strategies that account for document layout structure: bounding boxes, reading order, multi-column formats, stamps, signatures, and handwritten annotations
• Build serverless AI inference and orchestration pipelines using AWS Lambda, API Gateway, and Step Functions, enabling scalable and cost-efficient document processing workflows
• Collaborate with data engineers and backend platform teams to ensure clean, reliable data flows between source document ingestion, AI processing layers, and downstream data consumers
• Contribute to the design of AI-powered Data as a Service (DaaS) capabilities, enabling structured, AI-extracted document data to be consumed by internal analytics platforms and external API clients
• Champion observability and reliability in all AI systems: structured logging, inference latency monitoring, confidence score tracking, human-in-the-loop escalation workflows, and alerting for model degradation
• Partner with data scientists, cloud engineers, product managers, and business stakeholders to align AI model capabilities with real-world document processing requirements and accuracy targets
• Translate ambiguous business requirements into well-defined ML problem formulations, evaluation criteria, and iterative improvement plans
• Contribute to internal AI engineering standards, reusable pipeline components, and model governance documentation

Requirements:

• 5+ years of professional ML/AI engineering experience, with at least 2 years focused on LLMs, foundation models, or multimodal AI systems in production environments
• Extensive hands-on experience with AWS Bedrock for deploying, prompting, and fine-tuning foundation models across multimodal and text-based applications
• Deep proficiency with Amazon SageMaker for model training, hyperparameter optimization, hosted endpoint deployment, and pipeline orchestration
• Proven MLOps experience with MLflow on AWS: experiment tracking, model versioning, registry workflows, and integration with CI/CD systems
• Demonstrated advanced prompt engineering expertise across multiple paradigms: hierarchical prompting, context-aware and layout-aware prompting, few-shot and zero-shot learning, multi-turn dialogue, image-text alignment, and cross-attention prompt optimization
• Hands-on experience with AWS Textract and Amazon Rekognition for document extraction, OCR, table detection, and image analysis within automated ML workflows
• Experience building serverless AI pipeline architectures using AWS Lambda, API Gateway, and Step Functions
• Working knowledge of Amazon Elastic Inference and SageMaker optimization tools for inference cost and latency management
• Proficiency with AWS Deep Learning AMIs for rapid environment provisioning and reproducible ML experimentation
• Strong Python skills: PyTorch or TensorFlow, Hugging Face Transformers, LangChain or LlamaIndex, and supporting data science libraries
• Solid understanding of transformer architectures, attention mechanisms, tokenization, embedding models, and retrieval-augmented generation (RAG) patterns
• Experience implementing CI/CD pipelines for ML systems including automated model evaluation gates, deployment promotion workflows, and rollback strategies
• Industry experience in document-intensive domains such as transportation, logistics, financial services, healthcare, or legal, where document accuracy and extraction quality have direct operational impact
• Familiarity with transportation document types such as bills of lading, proof of delivery, rate confirmations, carrier invoices, inspection reports, or FMCSA compliance documents
• Experience with document foundation models or layout-aware vision-language models such as LayoutLM, LayoutLMv3, Donut, PaddleOCR, or LLaVA
• Familiarity with human-in-the-loop (HITL) feedback systems and active learning workflows for iterative model improvement using real-world production data
• Experience with vector databases (Amazon OpenSearch, Pinecone, Weaviate, or pgvector) and semantic search patterns for document retrieval and RAG pipelines
• Knowledge of model governance, responsible AI practices, confidence scoring, and auditability requirements for AI systems operating in regulated or high-stakes environments
• Experience working in fully remote, distributed engineering team