Senior Ai/ml Engineer Nlp & Generative Ai @ Siemens

We are looking for: Senior Software Engineer (AI/ML Engineer NLP & Generative AI)

Youll make an impact by:

• Design, develop, and optimize NLP-driven AI solutions using state-of-the-art models and techniques (NER, embeddings, summarization, etc.).
• Build and productionize RAG pipelines and agentic workflows to support intelligent, context aware applications.
• Fine-tune, prompt-engineer, and deploy LLMs (Open AI, Anthropic, Falcon, LLaMA, etc.) for domain-specific use cases.
• Collaborate with data scientists, backend developers, and cloud architects to build scalable AI first systems.
• Evaluate and integrate third-party models/APIs and open-source libraries for generative use cases.
• Continuously monitor and improve model performance, latency, and accuracy in production settings.
• Implement observability, performance monitoring, and explain ability features in deployed models.
• Ensure solutions meet enterprise-level requirements for reliability, traceability, and maintainability.

Use your skills to move the world forward!

• Masters or Bachelors degree in Computer Science, Machine Learning, AI, or a related field.
• 5+ years of overall experience in AI/ML, with at least 2+ years in NLP and 12 years in Generative AI.
• Strong understanding of LLM architectures, fine-tuning methods (LoRA, PEFT), embeddings, and vector search.
• Experience in designing and deploying RAG pipelines and working with multi-step agent architectures.
• Proficiency in Python and frameworks like Lang Chain, Transformers (Hugging Face),
• Llama Index, Smol Agents, etc.
• Familiarity with ML observability and explainability tools (e.g., TruEra, Arize, WhyLabs).
• Knowledge of cloud-based ML services like AWS Sagemaker, AWS Bedrock, Azure OpenAI Service, Azure ML Studio, and Azure AI Foundry.

Preferred Qualifications:

• Experience in integrating LLM-based agents in production environments.
• Understanding of real-time NLP challenges (streaming, latency optimization, multi-turn dialogues).
• Familiarity with LangGraph, function calling, and tools for orchestration in agent-based systems.
• Exposure to infrastructure-as-code (Terraform/CDK) and DevOps for AI pipelines.
• Domain knowledge in Electrification, Energy, or Industrial AI is a strong plus.