Generative AI Solutions Engineer Job in Pune at Siemens

Siemens is hiring for the role of Generative AI Solutions Engineer!

Responsibilities of the Candidate:

• Collaborate closely with stakeholders to gather, analyze, and meticulously document requirements for modern AI-driven projects.
• Translate complex business needs into clear, actionable functional and technical specifications for AI solutions.
• Conduct thorough feasibility studies and comprehensive Return on Investment (ROI) analyses for new AI initiatives, guiding strategic decision-making.
• Support change management efforts and drive the successful adoption of AI solutions across various business units.
• Act as a vital liaison, fostering effective communication and teamwork between diverse business units and technical development teams.
• Present insights, prototypes, and project results to collaborators in a clear, concise, and actionable manner, facilitating informed decisions.
• Continuously monitor and stay on top of emerging AI technologies, industry standard processes, and innovative approaches, especially within the Generative AI landscape.
• Design, deploy, and maintain scalable, enterprise-grade AI architectures and multi-step agent pipelines, demonstrating a strong understanding of end-to-end solution design patterns and standard processes for GenAI applications.
• Build, deploy, and maintain FastAPI endpoints specifically for GenAI agents.
• Monitor and continuously improve model performance, latency, and accuracy in production environments, ensuring enterprise-grade standards for reliability, traceability, and maintainability.

Requirements:

• 2–3 years of hands-on experience in the full lifecycle of AI development and operations, with a strong focus on generative AI solutions and agent implementation, including successful integration into production environments.
• Generative AI & Machine Learning: In-depth knowledge and hands-on experience with core GenAI components (e.g., Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, embeddings, agent systems) as well as foundational classical machine learning concepts.
• Cloud ML Platforms: Validated experience with leading cloud-based ML services such as Azure ML Studio, Azure OpenAI Service, AWS SageMaker, or AWS Bedrock.
• Containerization & Orchestration: Expertise with containerization technologies (e.g., Docker, Kubernetes, Azure Container Apps, AWS Fargate).
• DevOps & MLOps: Strong understanding and experience with GitHub workflows, CI/CD pipeline management, and robust deployment automation strategies.
• Programming & Frameworks: Proficiency in Python and familiarity with modern ML frameworks and agent frameworks (such as LangGraph and ADK).
• Agile Methodologies: Practical experience with agile development methodologies (e.g., Scrum).
• Problem-Solving: Strong troubleshooting and analytical problem-solving skills, with an ability to solve complex technical challenges effectively.
• Software Engineering Principles: Excellent software engineering skills, including a commitment to good coding practices, clean architecture, and maintainable code.