AI ENGINEERING ARCHITECT

Description du poste

Thco technology are seeking a highly experienced AI Engineering Architect to lead the design, architecture, development, and deployment of scalable, secure, and production-grade AI solutions.

The ideal candidate will combine strong software engineering, AI/ML engineering, solution architecture, cloud infrastructure, and technical leadership expertise. This individual will be responsible for translating business requirements and AI opportunities into robust technical architectures and ensuring that AI systems can move from experimentation and proof-of-concept to reliable enterprise production.

The AI Engineering Architect will work closely with product, engineering, data, infrastructure, and business teams to define AI strategies, establish technical standards, and build intelligent applications using modern approaches such as Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, machine learning, NLP, computer vision, and predictive analytics.

Missions

• AI & Solution Architecture:
• Design end-to-end architectures for AI-powered products, platforms, and enterprise solutions.
• Translate business and product requirements into scalable AI/ML technical architectures.
• Define architecture patterns for AI applications, including LLM applications, RAG systems, AI agents, recommendation engines, predictive models, and intelligent automation.
• Evaluate whether AI is the appropriate solution for specific business problems and recommend alternative approaches where necessary.
• Define technical architecture covering application, AI/ML, data, API, infrastructure, security, and integration layers.
• Develop architecture diagrams, technical specifications, system designs, and architectural decision records.
• Generative AI & LLM Engineering:
• Architect and implement solutions using Large Language Models and Generative AI technologies.
• Design and optimize Retrieval-Augmented Generation (RAG) architectures.
• Work with vector databases, embeddings, semantic search, document processing, chunking, reranking, and retrieval pipelines.
• Design and implement AI agents and multi-agent workflows where appropriate.
• Develop prompt engineering strategies and reusable prompt frameworks.
• Integrate commercial and open-source AI models through APIs and self-hosted infrastructure.
• Evaluate different LLMs based on accuracy, latency, cost, context window, security, and business requirements.
• Build AI systems capable of interacting with enterprise data, APIs, databases, and third-party systems.
• Hands-On Software Engineering:
• Write production-quality code for AI and backend systems.
• Lead the development of scalable APIs, services, and AI application components.
• Participate actively in architecture, coding, code reviews, debugging, testing, and deployment.
• Build microservices and backend services that integrate AI capabilities into enterprise applications.
• Machine Learning Engineering:
• Design and oversee machine learning pipelines from data preparation through model training, evaluation, deployment, and monitoring.
• Select appropriate machine learning approaches based on business requirements and available data.
• Work with supervised, unsupervised, and deep learning techniques where applicable.
• Design model-serving and inference architectures.
• Implement model versioning, experimentation, evaluation, and lifecycle management.
• Data & AI Infrastructure:
• Design architectures for data ingestion, processing, storage, transformation, and retrieval required by AI applications.
• Work with structured and unstructured data sources.
• Architect data pipelines supporting AI/ML workloads.
• Design and implement vector search and knowledge retrieval infrastructure.
• Work with databases, data warehouses, data lakes, and cloud storage technologies.