Artificial Intelligence Architect

Role Overview

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.
• Bachelors 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.