AI Technical Lead

Project descriptionLuxoft is initiating the development of a solution designed to generate investment insights based on sales and research materials. The solution will leverage advanced Agentic AI capabilities to significantly reduce the time required to prepare for client meetings and improve quality of the insights.ResponsibilitiesLead the architecture and implementation of production-grade LLM orchestration, multi-agent systems, and autonomous coding workflowsOversee the development of enterprise Retrieval-Augmented Generation (RAG) pipelines, semantic chunking strategies, and vector database integrationsImplement strict evaluation and observability frameworks to monitor production latency, API costs, system drift, and model accuracyDrive the design, deployment, and ongoing maintenance of secure, scalable, and resilient cloud systems on AWS leveraging ECS Fargate, Lambda, SQS, Aurora, and NeptuneOwn the end-to-end infrastructure lifecycle by embedding robust Infrastructure-as-Code (IaC) and deployment pipelines directly within the development workflowAct as the primary technical liaison between business stakeholders, product managers, and the engineering team to translate strategic goals into technical realitiesSkillsMust have10+ years of professional software development experience.Advanced proficiency in Python (asyncio, FastAPI) and TypeScript (Next.js/React, serverless execution layers)Hands-on experience building complex, stateful agentic workflows using LangGraph or LangChainProven track record architecting, provisioning, and managing your own production infrastructure on AWS, specifically utilizing ECS Fargate, Lambda, and SQSExperience defining cloud architecture programmatically using advanced Infrastructure-as-Code (IaC) tools like AWS CDK or Terraform (Python/TypeScript preferred)Experience managing relational databases (Aurora) alongside graph or vector backends (Neptune)Power-user fluency with advanced command-line AI interfaces (Claude Code CLI, GitHub Copilot CLI) with a deep understanding of prompt engineering and context window managementExperience operating in an agile setting, deploying and maintaining AI/LLM applications in a live, enterprise-scale production environmentAccountable for results, with excellent communication skills to mentor engineers and defuse technical frictionNice to haveHighly desirable: experience with AgentCore, AWS Neptune, Amazon API gatewayFamiliarity with ML fundamentals relevant to content generation (embeddings, tokenization, evaluation, fine tuning/LoRA, prompt+retrieval evaluation).OtherLanguagesEnglish: B2 Upper IntermediateSeniorityLeadLondon, United Kingdom of Great Britain and Northern IrelandReq. VR-124023Solution/Integration ArchitectureBCM Industry07/08/2026Req. VR-124023