We are looking for an Applied LLM Engineer to design, fine-tune, and integrate large language models into practical internal applications such as AI assistants, knowledge systems, and workflow automation tools.

You will play a key role in leveraging LLMs to build different AI Agents to enhance employee productivity, simplify information retrieval, and enable intelligent task automation.

• Job's Responsibilities Design and Architect LLM-Powered Internal Productivity Tools

• Design the overall system architecture for complex LLM applications, ensuring scalability, security, and seamless integration with existing data sources and internal APIs.

• Prototype and validate LLM applications through iterative testing with internal user groups.

• Select appropriate models & frameworks and create implementation roadmaps for LLM initiatives.

• Ensure solutions adhere to security, privacy, and compliance standards for internal data.

• Develop and Optimize RAG Systems for Enterprise Knowledge Management

• Build end-to-end Retrieval-Augmented Generation (RAG) pipelines, from document ingestion and chunking to vector indexing (e.g. using FAISS, Milvus, or tools from cloud providers etc.) and intelligent retrieval with re-ranking.

• Design and optimize retrieval strategies, including hybrid search and re-ranking mechanisms.

• Continuously experiment with and implement advanced techniques to improve retrieval accuracy, answer relevance, and system latency.

• Own the health and evolution of internal knowledge bases, ensuring information is accurate, up-to-date, and accessible Build and Maintain AI Agents for Workflow Automation

• Develop multi-step AI agents capable of executing tasks such as data entry, document generation, and system monitoring.

• Implement function calling and tool-use capabilities to integrate agents with internal software and APIs.

• Create orchestration workflows using frameworks like Lang Chain or Lang Graph to manage agent execution.

• Monitor agent performance, log interactions, and implement fallback mechanisms for error handling.

• Iterate on agent design based on usability testing and operational feedback.

• Own the Deployment and MLOps Lifecycle for LLM Applications

• Build and maintain CI (Continuous Integration) /CD (Continuous Delivery) pipelines for automated testing, deployment, and rollback of LLM services using frameworks like FastAPI or Flask.

• Utilize cloud AI platforms (e.g., Azure AI, AWS Bedrock, Alibaba Bailian) or containerization (Docker, Kubernetes) to deploy and scale applications.

• Establish monitoring for application health, performance metrics, AI-specific concerns (e.g., hallucination rates, token usage), and operational costs.

Qualifications and:

Experience: 1.

Bachelor's degree or above in Computer Science, Artificial Intelligence, Electrical Engineering or a related field. 2. 5+ years of software engineering experience, with at least 2 years focused on building and deploying production applications using Large Language Models. 3.

Expert proficiency in Python and extensive practical experience with deep learning frameworks (Py Torch/Tensor Flow). 4.

Deep LLM Stack Expertise:

• Proven hands-on experience with the modern LLM development stack, including Lang Chain or Llama Index, vector databases (FAISS, Milvus), and embedding models.
• Deep, practical understanding of RAG architecture (BM25 + Vector DB + Re-rank), Agentic AI workflows (Re Act, Tool Use), and advanced prompt engineering patterns. 5.

Production Engineering Skills: Solid backend development experience with frameworks like FastAPI or Flask.

Familiarity with MLOps practices for generative AI and cloud platforms (AWS, Azure or Alibaba Cloud). 6.

Demonstrated ability to take ownership of complex projects from conception to production, making principled trade-offs between speed, quality, and cost.

Strong analytical and debugging skills. 7.

Excellent communication skills with the ability to explain complex technical concepts to diverse audiences and collaborate effectively with cross-functional teams.

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We believe that our IMPACT values – Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork – starts with us. IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play.

It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world.

We are looking for IMPACT Makers; exceptional people who turn sustainability ambitions into actions at the intersection of automation, electrification, and digitization.

We celebrate IMPACT Makers and believe everyone has the potential to be one.

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This position will be posted until filled.

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We mirror the diversity of the communities in which we operate, and ‘inclusion’ is one of our core values.

We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do.

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Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders.

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It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.

We are looking for an Applied LLM Engineer to design, fine-tune, and integrate large language models into practical internal applications such as AI assistants, knowledge systems, and workflow automation tools.

You will play a key role in leveraging LLMs to build different AI Agents to enhance employee productivity, simplify information retrieval, and enable intelligent task automation.

• Job's Responsibilities Design and Architect LLM-Powered Internal Productivity Tools

• Design the overall system architecture for complex LLM applications, ensuring scalability, security, and seamless integration with existing data sources and internal APIs.

• Prototype and validate LLM applications through iterative testing with internal user groups.

• Select appropriate models & frameworks and create implementation roadmaps for LLM initiatives.

• Ensure solutions adhere to security, privacy, and compliance standards for internal data.

• Develop and Optimize RAG Systems for Enterprise Knowledge Management

• Build end-to-end Retrieval-Augmented Generation (RAG) pipelines, from document ingestion and chunking to vector indexing (e.g. using FAISS, Milvus, or tools from cloud providers etc.) and intelligent retrieval with re-ranking.

• Design and optimize retrieval strategies, including hybrid search and re-ranking mechanisms.

• Continuously experiment with and implement advanced techniques to improve retrieval accuracy, answer relevance, and system latency.

• Own the health and evolution of internal knowledge bases, ensuring information is accurate, up-to-date, and accessible Build and Maintain AI Agents for Workflow Automation

• Develop multi-step AI agents capable of executing tasks such as data entry, document generation, and system monitoring.

• Implement function calling and tool-use capabilities to integrate agents with internal software and APIs.

• Create orchestration workflows using frameworks like Lang Chain or Lang Graph to manage agent execution.

• Monitor agent performance, log interactions, and implement fallback mechanisms for error handling.

• Iterate on agent design based on usability testing and operational feedback.

• Own the Deployment and MLOps Lifecycle for LLM Applications

• Build and maintain CI (Continuous Integration) /CD (Continuous Delivery) pipelines for automated testing, deployment, and rollback of LLM services using frameworks like FastAPI or Flask.

• Utilize cloud AI platforms (e.g., Azure AI, AWS Bedrock, Alibaba Bailian) or containerization (Docker, Kubernetes) to deploy and scale applications.

• Establish monitoring for application health, performance metrics, AI-specific concerns (e.g., hallucination rates, token usage), and operational costs.

Qualifications and:

Experience: 1.

Bachelor's degree or above in Computer Science, Artificial Intelligence, Electrical Engineering or a related field. 2. 5+ years of software engineering experience, with at least 2 years focused on building and deploying production applications using Large Language Models. 3.

Expert proficiency in Python and extensive practical experience with deep learning frameworks (Py Torch/Tensor Flow). 4.

Deep LLM Stack Expertise:

• Proven hands-on experience with the modern LLM development stack, including Lang Chain or Llama Index, vector databases (FAISS, Milvus), and embedding models.
• Deep, practical understanding of RAG architecture (BM25 + Vector DB + Re-rank), Agentic AI workflows (Re Act, Tool Use), and advanced prompt engineering patterns. 5.

Production Engineering Skills: Solid backend development experience with frameworks like FastAPI or Flask.

Familiarity with MLOps practices for generative AI and cloud platforms (AWS, Azure or Alibaba Cloud). 6.

Demonstrated ability to take ownership of complex projects from conception to production, making principled trade-offs between speed, quality, and cost.

Strong analytical and debugging skills. 7.

Excellent communication skills with the ability to explain complex technical concepts to diverse audiences and collaborate effectively with cross-functional teams.