AI Architect

About the role

We are looking for an experienced AI Architect to lead the design, development, and deployment of enterprise‑scale AI solutions across multiple business domains. The role blends technical leadership, strategic planning, and hands‑on architecture to deliver secure, scalable, and business‑aligned AI platforms.

Key responsibilities

• Define and drive enterprise AI architecture strategies aligned with business objectives.
• Design end‑to‑end AI/ML solutions, including data pipelines, model training, deployment, monitoring, and governance.
• Architect machine learning, deep learning, generative AI, Retrieval‑Augmented Generation (RAG), AI copilots, and agentic AI solutions.
• Establish architectural standards, best practices, and reusable frameworks for AI initiatives.
• Collaborate with data science and engineering teams to translate requirements into scalable AI solutions.
• Implement MLOps frameworks, CI/CD pipelines, model monitoring, observability, and automated retraining processes.
• Develop cloud‑agnostic AI architectures for cloud, hybrid, and on‑premises environments.
• Lead system design reviews, technical governance, and enterprise integration initiatives.
• Engage with clients and stakeholders to define AI roadmaps, conduct workshops, and provide strategic guidance.
• Mentor AI, data science, and engineering teams while driving technical excellence.

Required profile

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, Mathematics or related field.
• 10+ years of experience in software engineering, data engineering, machine learning, or solution architecture.
• 5+ years of experience architecting and delivering enterprise‑scale AI/ML solutions.
• Strong understanding of statistics, machine learning theory, optimization techniques, and data science methodologies.

Required skills

• Machine learning, deep learning, generative AI, Retrieval‑Augmented Generation (RAG), AI copilots, agentic AI.
• MLOps, CI/CD pipelines, model monitoring, observability, automated retraining.
• Cloud‑agnostic architecture across cloud, hybrid, and on‑premises environments.
• Autonomous multi‑agent AI systems with governance and human‑in‑the‑loop controls.
• Statistical analysis, optimization techniques, data science methodologies.
• Regression, classification, clustering, anomaly detection, ensemble methods, time‑series forecasting.
• Transformers, foundation models, large language models (LLMs).