Job description
Hiire is supporting Siemens Mobility i n the search for a Senior Backend to join a team focused on building enterprise-grade AI solutions with direct impact on critical business operations and workflows. This is an opportunity to work in a highly technical and multidisciplinary environment where AI, software engineering, and product thinking come together to create scalable solutions for real-world problems.
We are looking for someone with a hands-on mindset, strong production focus, and experience turning AI prototypes into robust and reliable systems. What You’ll Do Collaborate closely with engineering, operations, and business stakeholders to understand workflows, identify pain points, and design AI-driven solutions with measurable impact.
Translate complex requirements into scalable AI-enabled services and technical concepts. Contribute directly to the design and architecture of systems powered by LLMs and retrieval technologies. Build AI Systems for Production Develop and maintain scalable, production-grade AI services focused on: LLMs, RAG pipelines, Hybrid retrieval, Semantic search, Vector search, Agentic workflows Build backend APIs, data pipelines, and integrations while ensuring reliability, observability, and engineering best practices.
Own the full development lifecycle: architecture, implementation, testing, deployment, and monitoring. AI Evaluation, Prompt Engineering & Data Quality Design rigorous evaluation strategies for AI output quality, retrieval accuracy, and model behavior. Treat prompt engineering as an engineering and experimentation discipline.
Ensure high standards for data quality and contextual grounding across AI systems. Mentor junior team members on AI engineering, experimentation, and evaluation practices. What We’re Looking For 5+ years of experience in Backend Development. Strong proficiency in modern Python (3.11+), including: async/await, typing, Pydantic.
Ability to bridge experimental Data Science workflows with reliable and maintainable software engineering practices. Experience with: Docker, CI/CD, testing frameworks (pytest), code quality tooling (ruff, mypy), Git workflows and code reviews Nice to Have Experience building and operating AI solutions in production and enterprise environments.
Exposure to AI, NLP, or Information Retrieval. Hands-on experience with: LLMs, Prompt Engineering, RAG, GraphRAG, Vector databases / Open. Search, AI evaluation pipelines Experience with AWS Bedrock or Azure OpenAI. Familiarity with MCP (Model Context Protocol) and modern AI interface standards. If you’re looking for an opportunity to work on applied AI in a serious, scalable, and impactful environment, this could be a very exciting next step.