AI Architect

Job purpose

The AI Architect will lead design and delivery of AI solutions, with a strong focus on Generative AI, large language models (LLMs), AI agents, and core machine learning. The role combines architecture, hands-on prototyping, and close collaboration with business and engineering teams to turn AI opportunities into scalable, production-grade solutions.

Roles and Responsibilities

- Define AI/ML architecture and standards, and guide teams on best practices across the AI portfolio.
- Design end-to-end AI solutions, including agentic workflows, RAG systems, and ML models, that integrate with existing enterprise platforms.
- Lead technical design reviews, PoCs, and reference implementations for AI agents, LLM use cases, and MLOps pipelines.
- Collaborate with product, data, and engineering teams to translate business requirements into AI solution designs and roadmaps.
- Support presales/RFPs with solution architectures, effort estimates, and implementation plans when required.
- Mentor engineers and data scientists on AI architecture, coding standards, and operationalization of models.

Required experience

- 10+ years in data/AI/ML, including significant experience designing and deploying enterprise AI solutions.
- Proven experience with LLMs and Generative AI (RAG, fine-tuning, prompt design) and at least one major cloud AI platform (AWS, Azure, or GCP).
- Hands-on background in machine learning or deep learning using frameworks such as PyTorch or Tensor Flow.
- Strong understanding of MLOps concepts (model lifecycle, deployment, monitoring) and modern data platforms.

Core skills

- AI agents and agentic workflows (tool use, orchestration, and integration with APIs and enterprise systems).
- LLM and RAG architecture, vector databases, and evaluation of model quality and reliability.
- Cloud-native design, microservices, and containerization (e.g., Docker/Kubernetes) for AI workloads.
- Strong Python skills and familiarity with common AI/ML libraries and data engineering tools.