AI engineer

Project description


Luxoft 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.


Responsibilities

Oversee the development of enterprise Retrieval-Augmented Generation (RAG) pipelines, semantic chunking strategies, and vector database integrations


Implement strict evaluation and observability frameworks to monitor production latency, API costs, system drift, and model accuracy


Drive the design, deployment, and ongoing maintenance of secure, scalable, and resilient cloud systems on AWS leveraging ECS Fargate, Lambda, SQS, Aurora, and Neptune


Own the end-to-end infrastructure lifecycle by embedding robust Infrastructure-as-Code (IaC) and deployment pipelines directly within the development workflow


Act as the primary technical liaison between business stakeholders, product managers, and the engineering team to translate strategic goals into technical realities


Skills


Must have


10+ 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 LangChain


Proven track record architecting, provisioning, and managing your own production infrastructure on AWS, specifically utilizing ECS Fargate, Lambda, and SQS


Experience defining cloud architecture programmatically using advanced Infrastructure-as-Code (IaC) tools like AWS CDK or Terraform (Python/TypeScript preferred)


Experience managing relational databases (preferably Aurora) alongside graph or vector backends (such as 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 management


Experience operating in an agile setting, deploying and maintaining AI/LLM applications in a live, enterprise-scale production environment


Accountable for results, with excellent communication skills to mentor engineers and defuse technical friction


Nice to have


Highly desirable: experience with AgentCore, AWS Neptune, Amazon API gateway


Familiarity with ML fundamentals relevant to content generation (embeddings, tokenization, evaluation, fine tuning/LoRA, prompt+retrieval evaluation).


Other

Languages


English: B2 Upper Intermediate


Seniority


Senior