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Kantiv is an AI-powered software platform serving the architecture, engineering, and construction (AEC) industry, helping professionals manage marketing and revenue operations. We are seeking a Data Scientist to join our team and build intelligent, data-driven solutions that power our AI platform.
In this role, you will work with structured and unstructured data to develop AI applications, uncover insights, and improve the intelligence behind our products. You will collaborate closely with backend engineers, AI engineers, and product teams to experiment with new approaches, solve real-world problems, and translate data into actionable insights.
Key Responsibilities:
- Build and improve AI-powered applications using LLMs, foundation models, and modern AI APIs to solve real-world product problems
- Integrate commercial and open-source language models into applications, experimenting with prompting strategies, structured outputs, tool calling, and model selection
- Develop retrieval-augmented generation (RAG) pipelines to help AI systems retrieve and reason over relevant information from documents, databases, and unstructured data sources
- Build and experiment with AI agents and multi-step workflows that use tools, access relevant context, and execute tasks with appropriate controls
- Develop pipelines for document ingestion, text extraction, chunking, embeddings, metadata management, and semantic search
- Evaluate AI outputs for accuracy, relevance, consistency, and groundedness; build test datasets and evaluation workflows to identify failure cases
- Collaborate with backend engineers to integrate AI capabilities into APIs and existing product workflows
- Experiment with model selection, caching, retrieval strategies, and prompt optimization to improve latency, reliability, and inference costs
- Investigate issues in AI pipelines, troubleshoot unexpected model behavior, and monitor the quality and performance of AI-powered features
- Stay current with emerging AI techniques, evaluate relevant tools and frameworks, and work with product and engineering teams to translate customer problems into practical AI solutions
Requirements:
- 1–3 years of experience in AI engineering, machine learning engineering, software engineering, or a related field (strong internships and hands-on projects involving AI applications are also valuable)
- Proficiency in Python and the ability to write clean, modular, maintainable code
- Understanding of how LLMs work at an application level, including prompting, context windows, token limits, structured outputs, and common failure modes
- Familiarity with embeddings, vector search, document retrieval, and the fundamentals of building RAG-based applications
- Experience working with REST APIs, JSON, asynchronous operations, and integrating external services into applications
- Ability to work with structured and unstructured data, using tools such as Pandas and SQL where appropriate
- Ability to test application behavior systematically, investigate failures, and improve the reliability of AI-driven workflows
- Strong analytical thinking and the ability to break down ambiguous problems into practical engineering solutions
- Clear communication skills, openness to feedback, and the ability to work effectively with engineers and product stakeholders
Desirable Experience:
- Production LLM applications beyond simple prompt-based prototypes
- Hands-on experimentation with AI agents, tool calling, function calling, or multi-step reasoning workflows
- Familiarity with frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent tools
- Experience with vector databases or search technologies such as pgvector, Pinecone, Weaviate, or Elasticsearch
- Exposure to LLM evaluation frameworks, tracing, monitoring, and tools for debugging AI workflows
- Familiarity with Docker, cloud platforms, deployment workflows, or serving AI applications in production
- Experience extracting information from PDFs, long-form documents, or complex business records
- Experience building AI capabilities for business users, particularly where accuracy, data security, and reliability matter
- Interest in applying AI to proposals, project information, client relationships, marketing, and revenue operations in the AEC industry
