Senior Group Technical Architect

Maximum Salary (US): 211000Minimum Salary (US): 148000Job SummaryProject 1: Module Prints – AI-Powered Engineering Diagram AnalysisThe Module Prints project focuses on using Artificial Intelligence to automatically extract relevant technical information from engineering diagrams and module prints. The solution applies computer vision and machine learning techniques to identify components, annotations, dimensions, labels, and other engineering-specific information contained within complex drawings. The goal is to convert traditionally unstructured visual engineering data into structured, searchable information that can be used by downstream applications and engineering teams. Key skills involved in this project include computer vision, image processing, machine learning algorithms, object detection, and AI-based information extraction.Project 2: IDI – Intelligent Document IntelligenceThe Intelligent Document Intelligence (IDI) project uses AI to extract and interpret information from complex PDF documents related to vehicle accidents. The system is designed to identify important details such as the vehicles involved, accident location, crash site information, potential causes of the accident, contributing factors, and other relevant incident data. AI-based document understanding techniques are used to process both textual and visual content within PDFs and transform the extracted information into structured data for further analysis. Key technologies include document AI, natural language processing, computer vision, machine learning, OCR, and intelligent information extraction.Project 3: AI Agent for Codebeamer Requirement EnrichmentThis project involves developing an intelligent AI agent that assists engineers in creating and improving requirements within Codebeamer. The agent analyzes existing requirement statements, associated documentation, engineering diagrams, and relevant organizational knowledge to identify missing information, improve clarity, and enrich requirements with additional technical context. Advanced Retrieval-Augmented Generation (RAG) techniques can be used to retrieve relevant historical requirements, standards, specifications, and supporting documentation before generating recommendations. Computer vision capabilities can also enable the agent to understand diagrams and other visual engineering artifacts associated with a requirement. Key skills include advanced RAG architectures, AI agents, large language models, computer vision, semantic search, vector databases, prompt engineering, and enterprise knowledge integration.Key ResponsibilitiesProject 1: Module Prints – AI-Powered Engineering Diagram AnalysisThe Module Prints project focuses on using Artificial Intelligence to automatically extract relevant technical information from engineering diagrams and module prints. The solution applies computer vision and machine learning techniques to identify components, annotations, dimensions, labels, and other engineering-specific information contained within complex drawings. The goal is to convert traditionally unstructured visual engineering data into structured, searchable information that can be used by downstream applications and engineering teams. Key skills involved in this project include computer vision, image processing, machine learning algorithms, object detection, and AI-based information extraction.Project 2: IDI – Intelligent Document IntelligenceThe Intelligent Document Intelligence (IDI) project uses AI to extract and interpret information from complex PDF documents related to vehicle accidents. The system is designed to identify important details such as the vehicles involved, accident location, crash site information, potential causes of the accident, contributing factors, and other relevant incident data. AI-based document understanding techniques are used to process both textual and visual content within PDFs and transform the extracted information into structured data for further analysis. Key technologies include document AI, natural language processing, computer vision, machine learning, OCR, and intelligent information extraction.Project 3: AI Agent for Codebeamer Requirement EnrichmentThis project involves developing an intelligent AI agent that assists engineers in creating and improving requirements within Codebeamer. The agent analyzes existing requirement statements, associated documentation, engineering diagrams, and relevant organizational knowledge to identify missing information, improve clarity, and enrich requirements with additional technical context. Advanced Retrieval-Augmented Generation (RAG) techniques can be used to retrieve relevant historical requirements, standards, specifications, and supporting documentation before generating recommendations. Computer vision capabilities can also enable the agent to understand diagrams and other visual engineering artifacts associated with a requirement. Key skills include advanced RAG architectures, AI agents, large language models, computer vision, semantic search, vector databases, prompt engineering, and enterprise knowledge integration.Skill RequirementsConsolidated Technology StackProgramming & ScriptingPythonSQLTypeScript / JavaScriptREST APIsBash / Shell ScriptingArtificial Intelligence & Machine LearningMachine LearningDeep LearningGenerative AILarge Language Models (LLMs)Multimodal AIPrompt EngineeringFine-Tuning & Model EvaluationLLM PlatformsAzure OpenAIAnthropic ClaudeLlama 3.xHugging Face ModelsAI Agent FrameworksSemantic KernelLangChainLangGraphAutoGenCrewAIAgentic AI Design PatternsFunction Calling & Tool IntegrationRetrieval-Augmented Generation (RAG)Advanced RAG ArchitecturesGraph RAGHybrid SearchSemantic SearchKnowledge GraphsContext EnrichmentEnterprise Knowledge RetrievalDocument Intelligence & OCRAzure AI Document IntelligenceOCR Technologies (Azure OCR, Tesseract, EasyOCR, PaddleOCR)PDF Processing (PyMuPDF, PDFMiner, pdfplumber)Document ClassificationInformation ExtractionForm RecognitionLayout AnalysisNatural Language Processing (NLP)spaCyHugging Face TransformersNLTKNamed Entity Recognition (NER)Text ClassificationEntity ExtractionSummarizationSemantic SimilarityComputer Vision & Image ProcessingOpenCVPIL/PillowAzure AI VisionObject DetectionImage SegmentationEngineering Diagram AnalysisTechnical Drawing InterpretationAnnotation & Symbol DetectionVisual Data ExtractionDeep Learning FrameworksPyTorchTensorFlowScikit-learnDetectron2YOLOVision Transformers (ViT)LayoutLMDonutDocFormerVector Databases & SearchAzure AI SearchPineconeWeaviateFAISSChromaDBMilvusElasticsearchOpenSearchData Engineering & AnalyticsPandasNumPyPySparkDatabricksETL PipelinesData ModelingData LakesDelta LakeDatabasesSQL ServerPostgreSQLCosmos DBMongoDBEnterprise IntegrationsCodebeamerAzure DevOpsGitHubJiraConfluenceSharePointEngineering Knowledge RepositoriesCloud & MLOpsMicrosoft AzureAzure Machine LearningAzure FunctionsAzure StorageAzure Kubernetes Service (AKS)Azure Data FactoryMLflowDockerKubernetesCI/CD PipelinesGitHub ActionsAzure DevOps PipelinesDomain ExpertiseAutomative IndustryRequirement EngineeringRequirement TraceabilityRequirement EnrichmentEnterprise Knowledge ManagementAI-Powered Information ExtractionOther Requirements