AI Architect

About the Role

We are seeking a highly experienced AI Architect to design, architect, and lead the implementation of enterprise-scale AI/ML and Generative AI solutions. The ideal candidate will translate business requirements into secure, scalable, cost-effective AI architectures and provide technical leadership across AI, data, cloud, and engineering teams.

Responsibilities

• Design end-to-end architectures for AI/ML, Generative AI, LLM, RAG, and Agentic AI solutions.
• Define scalable, highly available, secure, and cloud-native AI platforms.
• Evaluate and select appropriate LLMs, SLMs, ML models, embeddings, vector databases, and AI frameworks.
• Design RAG pipelines, prompt engineering strategies, model integration, and multi-agent workflows.
• Lead AI solution architecture from requirements gathering through development, deployment, and optimization.
• Establish MLOps/LLMOps standards for CI/CD, model lifecycle management, monitoring, evaluation, and observability.
• Design integrations with enterprise applications, APIs, databases, data platforms, and third-party services.
• Ensure AI solutions address security, privacy, governance, compliance, responsible AI, and data protection requirements.
• Optimize AI systems for performance, scalability, latency, reliability, and cost.
• Provide technical leadership, architecture reviews, mentoring, and guidance to AI/ML and engineering teams.
• Collaborate with business, product, data, cloud, security, and engineering stakeholders to define AI roadmaps and use cases.
• Create architecture diagrams, technical documentation, reference architectures, and implementation standards.
• Stay current with emerging technologies in GenAI, LLMs, AI agents, model optimization, and cloud AI platforms.

Requirements

• 8+ years of experience in software engineering, AI/ML, data, or solution architecture.
• Strong hands-on experience with AI/ML and Generative AI architecture.
• Expertise in Python and AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or similar.
• Strong knowledge of LLMs, prompt engineering, embeddings, RAG, vector databases, and AI agents.
• Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, or similar.
• Strong experience with AWS, Azure, or GCP.
• Knowledge of Docker, Kubernetes, APIs, microservices, and distributed systems.
• Experience with MLOps/LLMOps, CI/CD, model monitoring, evaluation, and observability.
• Strong understanding of AI security, governance, privacy, and responsible AI practices.
• Excellent architecture, problem-solving, communication, and stakeholder-management skills.