Lead AI Architect - Infrastructure and Modelops

We are seeking a visionary and technically astute Lead AI Architect to lead the architecture and design of scalable AI systems and next-generation intelligent platforms. As a core member of the leadership team, you will be responsible for driving end-to-end architectural strategy, model optimisation, and AI infrastructure that powers mission-critical solutions across our product lines. This is a foundational role for someone passionate about architecting solutions involving RAG, SLMs/LLMs, multi-agent systems, and scalable model pipelines across cloud-native environments

Responsibilities:

• Define and own the AI/ML architectural roadmap, aligning with product vision and technical goals.
• Architect and oversee implementation of RAG-based solutions, LLM/SLM fine-tuning pipelines, and multi-agent orchestration.
• Lead design of model training and inference pipelines, ensuring scalability, modularity, and observability.
• Evaluate and select open-source and proprietary foundation models for fine-tuning, instruction tuning, and domain adaptation.
• Guide integration of vector databases, semantic search, and prompt orchestration frameworks (LangChain, LlamaIndex, etc. ).
• Ensure best practices in model deployment, versioning, monitoring, and performance optimisation (GPU utilisation, memory efficiency, etc. ).
• Collaborate with Engineering, DevOps, Product, and Data Science teams to bring AI features to production.
• Mentor mid-level engineers and interns; contribute to technical leadership and code quality.
• Maintain awareness of the latest research, model capabilities, and trends in AI.

Requirements:

• 3+ years of hands-on experience in AI/ML architecture and model deployment.
• Expert-level knowledge of Python and libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and FastAPI.
• Deep understanding of LLMs/SLMs, embedding models, tokenisation strategies, fine-tuning, quantisation, and LoRA/QLoRA.
• Proven experience with Retrieval-Augmented Generation (RAG) pipelines and vector DBs like FAISS, Pinecone, or Weaviate.
• Strong grasp of system design, distributed training, MLOps, and scalable cloud-based infrastructure (AWS/GCP/Azure).
• Experience with containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLFlow, W& B).
• Experience in building secure and performant REST APIs, deploying and monitoring AI services in production.

Nice-to-Have:

• Exposure to multi-agent frameworks, task planners, or LangGraph.
• Experience leading AI platform teams or architecting enterprise-scale ML platforms.
• Familiarity with Data Governance, Responsible AI, and model compliance requirements.
• Published papers, open-source contributions, or patents in the AI/ML domain.