Principal AI Architect

About the role

We are seeking a Principal AI Architect to own the AI dimension of assigned programs end‑to‑end, from requirement interpretation through architecture, build, integration and go‑live. The role combines strategic roadmap planning, solution architecture, hands‑on model development and governance of responsible AI practices.

Key responsibilities

• Own the AI dimension of assigned programs end‑to‑end, from requirement interpretation through architecture, build, integration and go‑live.
• Prioritize and sequence use‑case roadmaps based on business value and delivery feasibility, setting realistic delivery plans.
• Act as the primary technical point of contact in stakeholder sessions, building technical credibility and trust.
• Architect the data foundation (ingestion, storage, quality) required to support prioritized AI use cases.
• Select and design the AI/ML approach for each use case, such as NLP, text classification, machine translation, retrieval, vector similarity search, LLM‑based assistants, forecasting and decision‑support scoring.
• Design integration points into core business systems so AI outputs are production‑ready and directly usable in existing workflows.
• Build priority models and pipelines directly, and direct shared or contracted engineering support.
• Establish MLOps practices (versioning, CI/CD, monitoring) sized appropriately to each program’s scale.
• Validate delivered models against agreed success metrics and provide evidence for stakeholder sign‑off.
• Ensure AI components align with responsible‑AI principles, data governance, privacy, explainability and audit logging.

Required profile

• 8–11 years of experience in AI/ML engineering and architecture.
• Proven experience delivering AI solutions end‑to‑end in a lead architect or principal engineering capacity.
• Working knowledge of NLP, text classification, machine translation, retrieval, vector similarity search, LLM‑based assistants and forecasting.
• Practical experience designing and operating data pipelines (ETL/ELT, lakehouse‑style environments) for AI use cases.
• Strong stakeholder communication, scoping and prioritization skills.
• Experience in regulated, public‑sector or specialized‑domain environments is an advantage.
• Bachelor’s degree in Computer Science, Engineering, Data Science or related field; advanced degree a plus.

Required skills

• AI/ML engineering
• AI/ML architecture
• Natural Language Processing (NLP)
• Text classification
• Machine translation
• Information retrieval and vector similarity search
• LLM‑based assistants and chatbots
• Forecasting models
• Data pipeline design (ETL/ELT)
• Lakehouse‑style data environments
• MLOps (versioning, CI/CD, monitoring)
• Model validation and performance metrics
• Responsible AI principles
• Data governance and privacy compliance
• Explainability and audit logging