AI Agentic Solutions Architect/Engineer

About the Role We are seeking an experienced AI Agentic Solutions Architect to lead the design, development, and deployment of enterprise-grade AI solutions powered by Large Language Models (LLMs) and autonomous AI agents. This role is responsible for architecting intelligent systems that integrate AI models, enterprise data, APIs, and business applications to automate complex workflows while ensuring scalability, security, reliability, and governance. This is a hands-on architecture role requiring experience delivering production-ready AI solutions beyond proof-of-concepts. Key Responsibilities Design, architect, and implement production-grade AI Agentic solutions that address complex business challenges. Develop end-to-end AI workflows incorporating planning, reasoning, retrieval, memory management, tool orchestration, and response validation. Evaluate and recommend appropriate foundation models (e.g., GPT, Gemini, Claude, Llama, Mistral) based on business requirements, performance, cost, security, and scalability. Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, and enterprise knowledge repositories. Build AI agents capable of securely interacting with enterprise applications, APIs, databases, and external services. Define strategies for conversational memory, long-term context management, and knowledge persistence. Implement AI governance, guardrails, access controls, auditability, and human-in-the-loop approval mechanisms. Optimise AI solutions for latency, throughput, scalability, reliability, and cost efficiency. Establish monitoring, evaluation, benchmarking, and continuous improvement frameworks for AI systems. Collaborate closely with business stakeholders, product teams, engineers, and security teams to deliver enterprise AI capabilities. Requirements Experience Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related discipline. Minimum 10 years of experience in software engineering, AI/ML, cloud architecture, or enterprise solution architecture. At least 3–5 years of hands-on experience designing and deploying production-grade Generative AI and/or Agentic AI solutions. Proven track record of leading the architecture and implementation of enterprise-scale AI platforms from design through production deployment. Technical Skills Strong understanding of Large Language Models (LLMs), foundation models, and model selection strategies. Hands-on experience with AI orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or equivalent. Experience designing Retrieval-Augmented Generation (RAG) solutions using vector databases and embedding models. Experience integrating AI applications with enterprise APIs, databases, knowledge repositories, and cloud-native services. Strong knowledge of prompt engineering, structured outputs, function calling, agent workflows, and multi-agent architectures. Experience implementing memory management, tool orchestration, planning strategies, and autonomous AI workflows. Understanding of AI evaluation methodologies, hallucination mitigation, grounding techniques, and response validation. Experience implementing enterprise AI governance, security controls, RBAC, audit logging, and Responsible AI principles. Proficiency in Python and modern software engineering practices. Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform. Preferred Qualifications Experience deploying and optimising open-source LLMs. Knowledge of GPU sizing, model optimisation, quantisation, inference optimisation, and scalable serving architectures. Experience with Kubernetes, Docker, CI/CD, MLOps, and AI observability platforms. Familiarity with enterprise search platforms, vector databases, and knowledge graph technologies. Experience delivering AI solutions within healthcare, government, financial services, or other regulated industries. Success Profile The ideal candidate will: Have successfully delivered enterprise AI Agentic solutions into production. Demonstrate strong solution architecture capabilities across AI, cloud, and software engineering. Be able to balance model performance, scalability, security, governance, and cost considerations. Possess deep expertise in LLM orchestration, agent design, memory management, and enterprise AI integration. Communicate effectively with both technical and business stakeholders while drivi…