Generative AI Engineer

Title – GenAI Architect Location – London, UK (Hybrid : 2-3 days a week) Mode – Contractor or FTE - Open for both Domain – Retail \& eCommerce Job Description : We are seeking a highly skilled and visionary AI Architect to lead the technical strategy, design, and end-to-end deployment of advanced Generative AI solutions within our Retail and eCommerce operations. This role has a specialized focus on designing complex Multi-Agent Systems to automate sophisticated workflows, particularly within our Customer Contact Cente r.You will define overarching AI solution approaches, establish architectural patterns, and evaluate technical feasibility. By leveraging the comprehensive Azure Cloud Stack, Azure Databricks, and modern DevOps/LLMOps practices, you will architect, develop, and continuously deploy scalable, intelligent solutions from proof-of-concept to highly resilient production environment s.Key Responsibiliti * esMulti-Agent System Architecture: Lead the design, orchestration, and implementation of sophisticated multi-agent Gen AI systems (utilizing frameworks like LangGraph, AutoGen, or CrewAI) that collaborate to solve complex customer inquiries and backend retail workflow * s.Customer Contact Center Transformation: Architect intelligent Gen AI solutions tailored for the Customer Contact Center. Deploy multi-agent systems to handle dynamic conversational routing, real-time agent assist, personalized customer resolutions, and automated follow-ups to drive higher CSA * T.Azure Databricks Leadership: Leverage Azure Databricks as the foundational data and compute layer for AI pipeline orchestration, massive-scale data processing, feature engineering, and model managemen * t.Strategic Design \& Feasibility: Define AI solution approaches, design patterns, and architectural blueprints. Assess the technical feasibility of proposed AI/ML initiatives to guide Agile product prioritization, feature breakdowns, and overarching solution directio * n.Observability, Security \& Resiliency: Establish robust LLMOps and AI observability frameworks. Implement deep tracing for multi-agent reasoning, alongside monitoring for model performance. Ensure production-grade resiliency through multi-region cloud architectures, disaster recovery (DR) protocols, and secure enterprise secret managemen * t.End-to-End Development \& DevOps: Architect and drive the full software development lifecycle (SDLC) for AI solutions. Establish robust CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks to ensure seamless, secure, and continuous deployments across Azure environment s.Core Competencies \& Tech Sta ckCatego ryRequired Skills \& Technologi esCloud \& Infrastructu reComplete Microsoft Azure ecosystem (Azure OpenAI, AKS, App Service, Functions), Multi-region Architecture, Secret Management (Azure Key Vault ).DevOps \& CI/ CDAzure DevOps, GitHub Actions, Jenkins (or similar), Infrastructure as Code (Terraform, Bicep), Docker, Kubernetes, Git, automated testing tool s.Data \& Processing Platfor msAzure Databricks (Spark, Delta Lake), Vector Databases (Pinecone, Azure AI Search, Milvus), Distributed Data Systems (CosmosDB, Redis ).AI \& Multi-Agent Framewor ksLangChain, LlamaIndex, Semantic Kernel, LangGraph, AutoGen, CrewAI, multi-agent state/memory management. LLM – OpenAI, Claude mode lsObservability \& LLMO psLangSmith, Phoenix, Azure Application Insights, model drift/latency monitoring, Prompt Engineering, Evaluation framework s.Domain Experti seRetail, eCommerce customer lifecycle, Customer Contact Center AI implementation, Agile Product Management, CSAT/KPI calculatio n. Required Qualifications \& Experie * nceExperience: Minimum 8-10+ years in software, data, or cloud architecture, with a heavy focus on Generative AI, machine learning, and end-to-end system deployments over the last 6+ yea * rs.Production Deployments: At least 2 successfully developed and deployed end-to-end AI products/projects in Producti o * n. End-to-End DevOps Mastery: Proven, hands-on experience driving full-lifecycle project development using modern DevOps methodologies, CI/CD pipelines, and infrastructure automation on the Azure sta * ck.Multi-Agent Expertise: Deep experience designing and deploying multi-agent architectures, understanding how to manage state, memory, and tool-use across cooperating AI agents in a production environme * nt.Domain Expertise: Strong understanding of the Retail and eCommerce industry and demonstrated experience implementing Gen AI solutions within Customer Contact Centers to optimize workforce efficien * cy.Data Platforms: Strong background in Azure Databricks for distributed data processing and AI workload manageme nt.What Will Make You Stand * OutExperience building proprietary enterprise tools or platforms that manage multi-agent conversation state and conte * xt.A strong background in managing LLM model migrations and optimizations for cost, latency, and performan * ce.Deep expertise in defining, calculating, and designing incentive frameworks or business performance metrics directly tied to AI project succe ss.