Senior AI Engineer
Siemens Energy
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Resumo da oferta
Siemens Energy seeks a Senior AI Engineer to design, build, deploy, and improve enterprise AI applications across Azure and AWS, in a hybrid environment. You will own the full engineering lifecycle, mentor juniors, and promote best practices within the Transformation of Industry division.
The role emphasizes production-grade AI solutions, cloud-native design, and secure, scalable architectures with OpenShift and Terraform as advantageous skills.
Qualificações
- Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.
- 8+ years of software engineering experience delivering production-grade applications.
- 5+ years 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 with FastAPI or similar frameworks.
- Hands-on experience with Azure and AWS, including AI and cloud-native services.
- Experience with Docker, Kubernetes, CI/CD and DevOps practices for enterprise apps.
- Knowledge of MLOps, AI observability, security, and operationalization of AI solutions.
- Experience with Snowflake, SQL, APIs, data integration, vector databases, and enterprise architectures.
- Ability to design scalable, secure software with strong fundamentals.
- Mentoring experience, code reviews, and driving best practices.
- OpenShift and Terraform are a strong advantage.
- Excellent analytical, problem-solving, and stakeholder-management skills.
- Ability to work in a globally distributed, cross-functional team.
Responsabilidades
- Design, develop, test, and deploy scalable AI applications using Generative AI, LLMs, and RAG frameworks.
- Build production-ready AI services and reusable components with Python, REST APIs, FastAPI, and microservices.
- Design end-to-end architectures integrating AI with enterprise apps, data platforms, and workflows.
- Develop cloud-native AI solutions on Azure and AWS considering scalability and security.
- Build and manage CI/CD pipelines for AI apps, model components, and deployments.
- Apply modular design, automated testing, code reviews, observability, and secure coding practices.
- Manage full lifecycle of AI apps from data ingestion to deployment and optimization.
- Maintain and improve existing AI solutions for reliability and performance.
- Implement guardrails, access controls, logging, and monitoring for enterprise AI.
- Collaborate with product managers, data engineers, cybersecurity, and business stakeholders.
- Lead design discussions, evaluate architectures, and communicate decisions clearly.
- Mentor junior developers and promote engineering best practices.
- Promote reusable patterns across AI, MLOps, and cloud architecture.
- Research emerging AI technologies and assess adoption value.
Conhecimentos
Formação académica
Ferramentas
Descrição da oferta de emprego
As a Senior AI Engineer, you will design, build, deploy, and continuously improve enterprise-grade AI applications that support critical business processes within the Transformation of Industry division.
As a Senior AI Engineer, you will design, build, deploy, and continuously improve enterprise-grade AI applications that support critical business processes within the Transformation of Industry division.
The role combines advanced AI engineering with strong software architecture and cloud-native application development. You will work hands-on with Generative AI, Large Language Models, Retrieval-Augmented Generation, APIs, microservices, and production AI platforms across both Microsoft Azure and Amazon Web Services.
You will take ownership of the complete engineering lifecycle, including solution design, development, integration, automated deployment, production monitoring, troubleshooting, and continuous improvement. You will also provide technical guidance to junior AI developers and help establish strong engineering practices across the team.
The ideal candidate has a strong foundation in software engineering, proven experience delivering production-grade AI solutions, and practical expertise across Azure and AWS. Experience with Red Hat OpenShift or similar enterprise Kubernetes platforms will be an advantage.
- 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 that integrate AI capabilities with enterprise applications, data platforms, identity services, and business workflows.
- Develop cloud-native AI solutions across Microsoft Azure and Amazon Web Services, selecting appropriate 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 such as modular design, automated testing, code reviews, observability, version control, secure coding, and technical documentation.
- Manage the full lifecycle of AI applications, from data ingestion and preprocessing through orchestration, API integration, deployment, monitoring, and ongoing optimization.
- Maintain, troubleshoot, and enhance existing AI solutions to ensure reliability, availability, performance, and business continuity.
- Implement appropriate 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 robust technical solutions.
- Lead technical design discussions, evaluate alternative architectures, and communicate engineering decisions, dependencies, risks, and trade-offs clearly.
- Mentor junior AI developers through technical coaching, pair programming, design guidance, code reviews, and structured 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 their adoption where they provide measurable business or technical value.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
- 8+ years of software engineering experience, with proven delivery of production-grade applications.
- 5+ years of hands-on experience designing, developing, and deploying AI/ML solutions, including Generative AI and Retrieval-Augmented Generation (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 Amazon Web Services (AWS), including AI, application, and cloud-native services.
- Experience with Docker, Kubernetes, CI/CD pipelines, and DevOps practices for deploying and operating enterprise applications.
- Good understanding of MLOps, AI observability, security, and operationalization of AI solutions.
- Experience working with Snowflake, SQL, APIs, data integration, vector databases, and enterprise application architectures.
- Ability to design scalable, secure, and maintainable software solutions with strong software engineering fundamentals.
- Proven experience mentoring junior engineers, conducting code reviews, and driving engineering best practices.
- Experience with OpenShift and Terraform is a strong advantage.
- Strong analytical, problem-solving, communication, documentation, and stakeholder-management skills.
- Ability to work within a globally distributed, cross-functional engineering environment.
Our Transformation of Industry division is decarbonizing the industrial sector. Increasing electrification and efficiency are key, and demand for green hydrogen and derivative fuels will rise. We enable the decarbonization of the industrial sector and the transition to sustainable processes by building on a strong industrial customer base, a global network, diverse technologies, and integrated execution capabilities.
The Data & Artificial Intelligence team develops and operates digital and AI solutions that support business transformation. The team brings together AI engineering, data, cloud, architecture, and software development capabilities to convert business requirements into secure, scalable, and operational solutions.
At Siemens Energy, we are more than just an energy technology company. With +100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world’s electricity generation.
Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.
Find out how you can make a difference at Siemens Energy: https://www.siemens-energy.com/employeevideo
Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion, and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character—no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.
- 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
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