LLM Engineer – OpenAI & LangChain

Drive cutting-edge AI innovation in the insurance industry as an LLM Engineer. Use OpenAI, LangChain, and Azure to build scalable, production-ready Generative AI systems. Work on advanced RAG pipelines and agentic workflows in a hybrid setup. Strong coding and real-world deployment skills are key.

• Salaried: $60-75 per hour.

• Incorporated Business Rate: $75-90 per hour.

• 5-month contract with the potential for permanent employment.

• Full-time position: 37.50 hours per week.

• Weekday schedule from 9 am to 5 pm.

• Hybrid work: in-office Tuesday to Thursday.

• Design, build, and deploy LLM-powered applications using OpenAI APIs, LangChain, and related frameworks.

• Develop Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and chatbot systems.

• Fine-tune and optimize LLMs to support business use cases with minimal hallucinations.

• Apply prompt engineering and evaluation techniques to improve model outputs.

• Integrate solutions with Azure, Databricks, Azure Cognitive Search, and VectorDBs (FAISS, Pinecone).

• Collaborate cross-functionally to scope, prototype, and productionize AI systems.

• Employ LLMOps and MLOps practices for deployment, monitoring, and lifecycle management.

• Stay up to date on trends in GenAI, LLMs, and open-source tools.

• Bachelor's degree in computer science, computer engineering, or a related technical field.

• Proficiency in Python and related libraries.

• 2+ years of hands-on experience in NLP, LLMs, and Generative AI.

• Strong understanding of prompt engineering and LLM fine-tuning.

• Experience with OpenAI APIs, LangChain, and Retrieval-Augmented Generation (RAG) architectures.

• Working knowledge of Model Context Protocol (MCP) and its application in LLM systems.

• Experience with cloud platforms such as Azure (preferred), AWS, or GCP.

• Hands-on experience with Databricks and Azure Cognitive Search.

• Familiarity with Vector Databases (e.g., FAISS, Pinecone, Weaviate) and agent frameworks (e.g., Autogen).

• Experience with DevOps/MLOps tools such as Git, Docker, MLflow, and Kubernetes.

• Strong communication skills with the ability to translate technical concepts into business value.

• Nice to have: Experience with Streamlit, Flask, or JavaScript for building interactive frontends.

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