AI Architect

We are seeking an accomplished AI Architect with 15+ years of technology experience to define and deliver enterprise-scale AI solutions spanning machine learning, Generative AI, Agentic AI, engineering intelligence, and CAD drawing understanding. The role combines architecture leadership with hands-on technical depth to convert complex business and engineering problems into secure, scalable, production-ready solutions across cloud, on-premises, hybrid, and edge environments. 

Role mandate: Own the journey from business problem and engineering data to AI architecture, PoC, industrialization, governance, and measurable value. 

Key Responsibilities 

• Define end-to-end enterprise architectures for AI/ML, Generative AI, Agentic AI, computer vision, multimodal AI, NLP, forecasting, and predictive analytics. 

• Architect agentic AI solutions with planners, specialized agents, tool use, memory, orchestration, human-in-the-loop controls, guardrails, and observability. 

• Design AI pipelines for CAD and engineering drawings, including 2D/3D CAD, P&IDs, schematics, layouts, technical diagrams, and associated metadata. 

• Lead approaches for drawing ingestion, raster/vector processing, OCR, symbol detection, object and relationship extraction, drawing classification, redline/change detection, and engineering document retrieval. 

• Integrate AI with CAD/PLM/BIM/GIS/document-management ecosystems and engineering formats such as DWG, DXF, DGN, IFC, STEP, IGES, PDF, and image-based drawings, where applicable. 

• Design RAG and knowledge-driven solutions using embeddings, vector databases, graph databases, knowledge graphs, semantic search, and enterprise content repositories. 

• Evaluate and select models, vision-language models, frameworks, model-serving platforms, vector stores, graph technologies, and cloud or on-premises AI services. 

• Define scalable AI platforms covering ingestion, data preparation, feature/model development, APIs, inference, workflow orchestration, monitoring, and enterprise integration. 

• Establish MLOps and LLMOps practices covering experiment tracking, evaluation, model and prompt versioning, deployment, monitoring, drift, feedback, and governance. 

• Provide guidance on GPU infrastructure, edge AI, model compression, quantization, pruning, inference optimization, latency, throughput, and cost. 

• Ensure security, privacy, responsible AI, intellectual-property protection, explainability, reliability, and regulatory compliance are embedded by design. 

• Lead architecture reviews, customer workshops, technology evaluations, PoCs, proposal solutioning, estimation, and technical defense discussions. 

• Mentor AI engineers, data scientists, ML engineers, software engineers, and solution architects across multiple delivery teams. 

Core Architecture Domains 

Domain 

Expected Capability 

Generative & Agentic AI 

LLMs, RAG, multimodal AI, autonomous and semi-autonomous agents, multi-agent orchestration, tool integration, memory, evaluation, guardrails, and human oversight. 

CAD & Engineering AI 

CAD and drawing understanding, OCR, vision-language models, geometry and symbol extraction, BOM/tag linkage, P&ID intelligence, change detection, drawing search, and engineering knowledge graphs. 

AI/ML & Analytics 

Machine learning, deep learning, computer vision, NLP, anomaly detection, time-series forecasting, predictive maintenance, and optimization. 

AI Platforms 

Data ingestion, model development, feature pipelines, API/microservice architecture, model serving, GPU platforms, observability, and enterprise integration. 

MLOps / LLMOps 

CI/CD, experiment tracking, registries, evaluation, prompt/model lifecycle, monitoring, drift detection, feedback loops, and governance. 

Enterprise Architecture 

Cloud, on-premises, hybrid and edge patterns; security; integration; scalability; resilience; cost optimization; and reusable reference architectures. 

Required Technical Skills 

AI, GenAI & Agentic AI 

• Python; PyTorch and/or TensorFlow; Hugging Face ecosystem 

• LLMs, embeddings, RAG, multimodal and vision-language models 

• Agent frameworks, workflow orchestration, tool/function calling, memory and evaluation 

• Fine-tuning, prompting, model evaluation, safety, guardrails and optimized inference 

CAD, Drawings & Engineering Data 

• Hands-on understanding of CAD/engineering drawing structures, layers, blocks, symbols, geometry and metadata 

• Experience with OCR, document AI, computer vision and VLM-based extraction from complex technical drawings 

• Integration patterns for CAD/PLM/BIM/GIS and engineering document repositories 

• Familiarity with common formats such as DWG, DXF, DGN, IFC, STEP, IGES, PDF and raster images 

Data, Cloud & Platforms 

• One or more of Azure, AWS or Google Cloud; Databricks or Microsoft Fabric is advantageous 

• Vector databases, graph databases/knowledge graphs, relational/NoSQL and time-series platforms 

• Docker, Kubernetes, APIs, microservices, event-driven integration and GPU infrastructure 

• Hybrid, on-premises and edge deployment architectures 

MLOps, LLMOps & Governance 

• CI/CD for AI, experiment tracking, model registry, prompt/model versioning and automated deployment 

• Quality evaluation, telemetry, model monitoring, hallucination and drift detection, and feedback management 

• Responsible AI, privacy, cybersecurity, access control, lineage, auditability and model risk governance 

Experience & Qualifications 

• 15+ years of overall experience in software, data, cloud, engineering systems, analytics, or AI-enabled transformation. 

• At least 5 years of significant AI/ML, advanced analytics, or intelligent automation experience, including architecture ownership. 

• Proven delivery of enterprise-scale AI/ML or Generative AI solutions in production. 

• Demonstrated experience designing Agentic AI, RAG, multimodal, computer vision, or knowledge-graph solutions. 

• Experience solving use cases involving CAD drawings, P&IDs, schematics, engineering documents, asset information, or industrial data is strongly preferred. 

• Strong understanding of enterprise integration, APIs, data architecture, security, cloud and distributed systems. 

• Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, Data Science, or a related discipline. 

Leadership & Consulting Expectations 

• Translate ambiguous business and engineering challenges into a prioritized AI roadmap and defensible target architecture. 

• Communicate complex AI concepts clearly to engineering teams, business leaders, customers, security teams, and executive stakeholders. 

• Make pragmatic build-versus-buy and model/platform decisions based on accuracy, scalability, latency, security, maintainability, and total cost. 

• Drive architecture governance while enabling rapid experimentation and reusable accelerators. 

• Coach cross-functional teams and promote disciplined engineering, documentation, evaluation, and responsible AI practices. 

Preferred Industry Exposure 

Industrial manufacturing, energy, utilities, engineering and construction, automotive, aerospace, asset-intensive operations, digital twins, IoT, predictive maintenance, or engineering modernization. 

Success Measures 

• AI solutions move from PoC to production with clear quality, safety, adoption, performance, and business-value metrics. 

• CAD and engineering intelligence use cases reduce manual interpretation, improve retrieval and traceability, and accelerate engineering workflows. 

• Agentic AI workflows demonstrate measurable task completion, groundedness, control, observability, and human accountability. 

• Reusable architectures, components, and governance patterns shorten delivery cycles across programs. 

• Platforms meet agreed standards for reliability, scalability, security, latency, and cost. 

Ideal Candidate Profile 

The ideal candidate is an architect who can operate across the complete value chain: Business and engineering problem → data and drawings → model/LLM/VLM selection → Agentic AI and knowledge architecture → PoC → production deployment → MLOps/LLMOps → governance → measurable business value. The individual should be strategically strong, technically hands-on, and comfortable challenging assumptions while guiding teams toward practical, production-grade implementations.