Senior AI Backend Engineer - Siemens Mobility

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 / OpenSearch, 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.