ai engineer for industrial decarbonization

Siemens Energy develops energy systems and technologies that support reliable, sustainable energy supply and the energy transition. Its Transformation of Industry division works to decarbonize the industrial sector and enable the transition to sustainable processes. The Data & Artificial Intelligence team develops and operates digital and AI solutions that support business transformation.

• Design, develop, test, and deploy scalable, secure, and maintainable AI applications using Generative AI, Large Language Models, and Retrieval-Augmented Generation frameworks
• Engineer production-ready AI services and reusable components using Python, REST APIs, FastAPI, and microservice-based architectures
• Design end-to-end solution architectures integrating AI with enterprise applications, data platforms, identity services, and business workflows
• Develop cloud-native AI solutions across Microsoft Azure and Amazon Web Services, selecting services based on scalability, security, performance, operational, and cost requirements
• Build and manage CI/CD pipelines for AI applications, model components, infrastructure, testing, and production releases
• Apply software engineering practices including modular design, automated testing, code reviews, observability, version control, secure coding, and technical documentation
• Manage AI application lifecycles from data ingestion and preprocessing through orchestration, API integration, deployment, monitoring, and ongoing optimization
• Maintain, troubleshoot, and enhance existing AI solutions to support reliability, availability, performance, and business continuity
• Implement guardrails, access controls, evaluation mechanisms, logging, and monitoring for enterprise AI applications
• Collaborate with product managers, architects, data engineers, application teams, cybersecurity teams, and business stakeholders to translate business requirements into technical solutions
• Lead technical design discussions, evaluate architectures, and communicate engineering decisions, dependencies, risks, and trade-offs
• Mentor junior AI developers through technical coaching, pair programming, design guidance, code reviews, and knowledge sharing
• Promote engineering standards and reusable patterns across AI development, MLOps, DevOps, cloud architecture, and secure software delivery
• Research and assess emerging AI technologies, frameworks, and engineering approaches, and recommend adoption where they provide business or technical value

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field
• At least 8 years of software engineering experience, including delivery of production-grade applications
• At least 5 years of hands-on experience designing, developing, and deploying AI/ML solutions, including Generative AI and RAG architectures
• Strong Python engineering skills and experience building scalable REST APIs and microservices using FastAPI or similar frameworks
• Hands-on experience with both Microsoft Azure and AWS, including AI, application, and cloud-native services
• Experience with Docker, Kubernetes, CI/CD pipelines, and DevOps practices for enterprise applications
• Understanding of MLOps, AI observability, security, and operationalization of AI solutions
• Experience with Snowflake, SQL, APIs, data integration, vector databases, and enterprise application architectures
• Ability to design scalable, secure, and maintainable software solutions based on strong software engineering fundamentals
• Experience mentoring junior engineers, conducting code reviews, and driving engineering best practices
• Strong analytical, problem-solving, communication, documentation, and stakeholder-management skills
• Ability to work in a globally distributed, cross-functional engineering environment
• Будет плюсом: OpenShift, Terraform

• Flexible and hybrid working environment
• Opportunities for continuous professional and personal development
• Collaborative and inclusive international work environment
• Access to learning, training, and knowledge-sharing opportunities
• Competitive local benefits
• Opportunities to work with innovative technologies and contribute to impactful projects