Senior LLM Engineer

About the Role: The Senior LLM Engineer will lead the design, development, and deployment of complex, autonomous agentic AI solutions within production environments, working across multi-agent frameworks and orchestrating large language models. The position is for scalable applied AI, with a focus on delivering robust and fair agentic systems that deliver organizational value, autonomy, and operational efficiency. Responsibilities: Architect, design, and deploy scalable agentic AI systems that coordinate LLMs, multi-agent collaboration, and external tool integrations through advanced tool calling. Develop, maintain, and optimize AI-driven pipelines utilizing frameworks such as LangGraph, ADK and LangChain, enhancing data inference and orchestration. Integrate agentic AI solutions into enterprise workflows, automating complex business functions (e.g., customer service, supply chain, decision support). Proactively address system evaluation, debug AI agent workflows, and ensure compliance with emerging regulatory and ethical guidelines. Promote security, transparency, fairness, and reliability across all deployed agentic systems. Mentor junior engineers, contribute to cross-functional teams, and champion the adoption of state-of-the-art LLM technologies and practices. Requirements: At least 1 year of experience with LangGraph (must have) 4+ years of overall experience working on industry projects Experience with LangChain, and other agentic orchestration frameworks. Deep proficiency in designing and deploying multi-agent frameworks and sophisticated tool-calling or API orchestration pipelines. Design and implement continuous monitoring systems for autonomous AI agents, ensuring performance, reliability, adherence to ethical guardrails, and optimal cost/latency in production. Develop and execute scalable LLM evaluation methodologies (including code-based, LLM-as-a-Judge) to rigorously assess model quality, safety, and goal alignment. Proficiency in Python Familiarity with retrieval-augmented generation (RAG) and modern vector databases (Pinecone, Weaviate). Experience with cloud platforms (AWS, GCP, Azure), MLOps practices, and production-grade deployment. Strong understanding of data privacy, model security, and compliance with current AI regulations and frameworks. Preferred Skills Experience with Hugging Face, Neo4j, or knowledge graphs. Background in autonomous decision-making systems, anomaly detection, and dynamic process optimization. Track record of publishing, presenting, or open-sourcing agentic AI innovations. Strong problem-solving aptitude, collaborative mindset, and excellent communication skills. About the Role: The Senior LLM Engineer will lead the design, development, and deployment of complex, autonomous agentic AI solutions within production environments, working across multi-agent frameworks and orchestrating large language models. The position is for scalable applied AI, with a focus on delivering robust and fair agentic systems that deliver organizational value, autonomy, and operational efficiency. Responsibilities: Architect, design, and deploy scalable agentic AI systems that coordinate LLMs, multi-agent collaboration, and external tool integrations through advanced tool calling. Develop, maintain, and optimize AI-driven pipelines utilizing frameworks such as LangGraph, ADK and LangChain, enhancing data inference and orchestration. Integrate agentic AI solutions into enterprise workflows, automating complex business functions (e.g., customer service, supply chain, decision support). Proactively address system evaluation, debug AI agent workflows, and ensure compliance with emerging regulatory and ethical guidelines. Promote security, transparency, fairness, and reliability across all deployed agentic systems. Mentor junior engineers, contribute to cross-functional teams, and champion the adoption of state-of-the-art LLM technologies and practices. Requirements: At least 1 year of experience with LangGraph (must have) 4+ years of overall experience working on industry projects Experience with LangChain, and other agentic orchestration frameworks. Deep proficiency in designing and deploying multi-agent frameworks and sophisticated tool-calling or API orchestration pipelines. Design and implement continuous monitoring systems for autonomous AI agents, ensuring performance, reliability, adherence to ethical guardrails, and optimal cost/latency in production. Develop and execute scalable LLM evaluation methodologies (including code-based, LLM-as-a-Judge) to rigorously assess model quality, safety, and goal alignment. Proficiency in Python Familiarity with retrieval-augmented generation (RAG) and modern vector databases (Pinecone, Weaviate). Experience with cloud platforms (AWS, GCP, Azure), MLOps practices, and production-grade deployment.