AI Developer; Mid-level

Overview

We are a leading global software company at the forefront of computer-aided design, 3D modeling, and simulation, helping innovative manufacturers worldwide design better products—faster and more efficiently.

We are looking for a Software Engineer with strong hands-on experience in Generative AI and Agentic AI development to build and enhance AI-powered capabilities across the Siemens Software portfolio. In this role, you will focus on implementing, integrating, and optimizing AI solutions using large language models (LLMs), agent frameworks, and scalable data pipelines.

The ideal candidate is a practitioner who enjoys writing production-quality code, integrating AI models with real-world data systems, and iterating on solutions to improve accuracy, performance, and reliability of AI-driven applications.

• Design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs) and Agentic AI systems for natural language understanding, question answering, contextual content generation, and autonomous task execution.
• Develop and maintain clean, well-structured, and well-documented APIs and services, primarily in Python, enabling reliable integration of AI and agentic capabilities with enterprise platforms.
• Implement agent-based workflows, including planning, tool usage, memory, and multi-step reasoning, integrating agents with internal tools, services, and data sources. Design, build, and operate data ingestion, transformation, and storage pipelines that handle structured, semi-structured, and unstructured data, supporting efficient modeling, querying, and retrieval for AI-driven use cases.
• Collaborate closely with data science and AI teams to product ionize models, prompts, agent strategies, and evaluation techniques into scalable, reliable software solutions.
• Integrate software components and systems, ensuring smooth deployment, monitoring, and operation in production environments.
• Identify and resolve performance, scalability, and reliability bottlenecks, optimizing AI applications and agent workflows for speed, cost, and resource efficiency.
• Document APIs, agent behaviors, data flows, and development processes to ensure maintainability and effective knowledge sharing.
• Stay current with advancements in Generative AI, Agentic AI, and software engineering, contributing ideas and improvements to continuously enhance development practices and product capabilities.

• Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, or a related field
• 3-5 years of professional experience in software development including SDLC
• 2+ years of hands-on experience developing AI applications using Large Language Models (LLMs) such as GPT-4, LLaMA, Anthropic Claude, or equivalent models.
• Practical experience with Agentic AI frameworks and patterns, including tools such as Semantic Kernel, Auto Gen, Microsoft Agentic Framework, Lang Chain, Lang Graph, OpenAI APIs, or similar technologies.
• Strong proficiency in Python, with proven experience building, testing, and maintaining production-grade APIs and services using frameworks such as FastAPI, Django, or Flask.
• Hands-on experience with vector databases (e.g., Chroma, Azure AI Search), graph databases (e.g., Neo4J) and No
  
  SQL databases (e.g., Mongo
  
  DB, Cosmos DB), including data modeling, query optimization, and application integration.
• Working knowledge of machine learning frameworks such as PyTorch or Tensor Flow, along with familiarity with MLOps practices (model lifecycle management, evaluation, monitoring).
• Experience with cloud platforms (AWS, Azure, or GCP) and deploying, scaling, and operating AI/LLM-based applications in production environments.
• Strong analytical and problem-solving skills, with attention to performance, reliability, and edge cases in real-world systems.
• Effective communication skills and the ability to collaborate closely with cross-functional teams, including data scientists, product managers, and platform engineers.

• Experience with containerization technologies (Docker, Kubernetes).
• Knowledge of CI/CD pipelines and automated testing frameworks
• Knowledge of data…