#WeAreIn to make sense of data by turning problems into opportunities. Are you in
Your Role
Key responsibilities in your new role
- Design, develop, and deploy Agentic AI solutions, including autonomous and multi-agent systems for complex business workflows
- Maintain and optimize Retrieval-Augmented Generation (RAG) pipeline susing vector databases, knowledge retrieval systems, and enterprise datasources
- Strong knowledge on AI assistants, co-pilots, and intelligent automation solutions using modern LLM frameworks and orchestration tools
- Architect and manage end-to-end LLM Ops pipelines, including model deployment, monitoring, evaluation, governance, and life cycle management
- Implement AI observability, guardrails, prompt management, model evaluation frameworks, and performance monitoring
- Build scalable AI infrastructure leveraging Kubernetes, Docker,GPU-based environments, distributed inference, and cloud-native architectures
- Design CI/CD pipelines for AI applications and ensure reliability,scalability, security, and cost optimization of AI platforms.
- Collaborate with Data Scientists, ML Engineers, Product Teams, and Business Stakeholders to translate requirements into production-ready AI solutions
- Evaluate emerging AI technologies, frameworks and research to drive innovation and best practices across the organization
- Mentor junior engineers and contribute to architecture, technical leadership, and AI platform strategy
Your Profile
Qualifications And Skills To Help You Succeed
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field
- Over all 8+ years experience in Software architecting / Software engineering
- 5+ years of experience Machine Learning Engineering, AI Engineering,or Platform Engineering
- Strong programming experience in Python and experience building production-grade applications
- Hands-on experience with Agentic AI frameworks such as LangGraph,LangChain, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, or similar technologies
- Strong understanding of LLM architectures, prompt engineering, RAGsystems, model evaluation, and fine-tuning techniques
- Experience building and operating LLMOps/MLOps platforms, includingmodel versioning, experimentation, monitoring, observability, and governance
- Expertise with vector databases such as Qdrant, Milvus, or similar technologies
- Strong experience with AI infrastructure technologies including Kubernetes, Docker, Ray, GPU orchestration, distributed systems, and inference optimization
- Knowledge of API development, microservices architecture, FastAPI,REST APIs, and event-driven systems
- Experience with monitoring and observability tools for AI work loads and production systems
Contact:
Gowri Shenoy, LinkedIn
#WeAreIn for driving decarbonization and digitalization.
As a global leader in semiconductor solutions in power systems and IoT, Infineon enables game-changing solutions for green and efficient energy, clean and safe mobility, as well as smart and secure IoT. Together, we drive innovation and customer success, while caring for our people and empowering them to reach ambitious goals. Be a part of making life easier, safer and greener.
Are you in
We are on a journey to create the best Infineon for everyone.
This means we embrace diversity and inclusion and welcome everyone for who they are. At Infineon, we offer a working environment characterized by trust, openness, respect and tolerance and are committed to give all applicants and employees equal opportunities. We base our recruiting decisions on the applicant´s experience and skills. Learn more about our various contact channels.
Please let your recruiter know if they need to pay special attention to something in order to enable your participation in the interview process.
Click here for more information about Diversity & Inclusion at Infineon.
