Venture Informatrix is a next-generation Insurance BPO service provider that blends deep insurance domain knowledge with cutting-edge Artificial Intelligence. We help insurance agencies, brokers, MGAs, and carriers streamline operations, enhance productivity, and scale efficiently—all while reducing operational costs and improving customer satisfaction.
With a global delivery model and AI-driven automation at the core, we deliver transformative back-office and front-office support services tailored to the insurance industry's evolving needs.
We are looking for an AI/LLM Engineer to build and own the intelligence layer of ACORDAI an AI-native insurance SaaS platform. You will design RAG pipelines, build the AI Copilot, and develop agentic workflows that automate complex insurance processes.
We are looking for an AI/LLM Engineer to build and own the intelligence layer of ACORDAI an AI-native insurance SaaS platform. You will design RAG pipelines, build the AI Copilot, and develop agentic workflows that automate complex insurance processes.
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Build and maintain RAG pipelines for document ingestion and retrieval.
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Extract structured coverage data from forms and documents.
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Develop the AI Copilot for agents, underwriters, and claims handlers.
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Design agentic workflows using LangGraph for multi-step insurance automation.
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Build risk scoring models combining structured data with LLM-derived signals.
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Write, version, and optimize prompts for OpenAI and other frontier models.
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Create evaluation pipelines to measure accuracy, latency, and reliability.
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Monitor AI features in production and iterate based on quality metrics.
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LLMs: OpenAI GPT-4o, Anthropic Claude
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Orchestration: LangChain, LangGraph
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Vector Search: pgvector, Pinecone
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Backend: Python, FastAPI
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Database: PostgreSQL, Redis
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Infrastructure: AWS / Azure, Docker
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3+ years building LLM-powered production applications.
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Hands-on experience with LangChain and/or LangGraph.
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Strong Python skills and experience with REST APIs.
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Experience designing and deploying RAG systems.
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Familiarity with vector databases and semantic search.
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Solid understanding of prompt engineering and context management.
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Experience building LLM evaluation pipelines.
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Experience in insurance, fintech, or legal tech.
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Familiarity with structured PDF data extraction.
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Knowledge of fine-tuning or open-source model deployment.
