Kantiv is a technology company revolutionizing the way professionals in the architecture, engineering, and construction (AEC) industry manage marketing and revenue operations. Our AI-powered software streamlines workflows, making it easier for teams to collaborate, innovate, and succeed.
We are looking for a Data Scientist to join our growing team and help build intelligent, data-driven solutions that power Kantiv’s AI platform. In this role, you will work with structured and unstructured data to uncover insights, develop agentic solutions, 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 turn data into actionable insights. This is an exciting opportunity for someone early in their data science career who enjoys solving challenging problems, experimenting with AI technologies, and building solutions that create measurable business impact.
AI Application Development: Build and improve AI-powered applications using LLMs, foundation models, and modern AI APIs to solve real-world product problems.
LLM Integration: Integrate commercial and open-source language models into applications, experimenting with prompting strategies, structured outputs, tool calling, and model selection.
RAG & Knowledge Systems: Develop retrieval-augmented generation (RAG) pipelines to help AI systems retrieve and reason over relevant information from documents, databases, and other unstructured data sources.
AI Agents & Workflows: Build and experiment with AI agents and multi-step workflows that use tools, access relevant context, and execute tasks with appropriate controls.
Data Processing & Retrieval: Develop pipelines for document ingestion, text extraction, chunking, embeddings, metadata management, and semantic search.
Evaluation & Reliability: Evaluate AI outputs for accuracy, relevance, consistency, and groundedness. Build test datasets and evaluation workflows to identify failure cases and improve system performance.
Backend Integration: Collaborate with backend engineers to integrate AI capabilities into APIs and existing product workflows, ensuring maintainability and reliability.
Performance & Cost Optimisation: Experiment with model selection, caching, retrieval strategies, and prompt optimisation to improve latency, reliability, and inference costs.
Monitoring & Debugging: Investigate issues in AI pipelines, troubleshoot unexpected model behaviour, and help monitor the quality and performance of AI-powered features.
Experimentation & Collaboration: 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.
Experience: 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.
Programming: Proficiency in Python and the ability to write clean, modular, maintainable code.
LLM Fundamentals: Understanding of how LLMs work at an application level, including prompting, context windows, token limits, structured outputs, and common failure modes.
RAG & Embeddings: Familiarity with embeddings, vector search, document retrieval, and the fundamentals of building RAG-based applications.
APIs & Backend Fundamentals: Experience working with REST APIs, JSON, asynchronous operations, and integrating external services into applications.
Data Handling: Ability to work with structured and unstructured data, using tools such as Pandas and SQL where appropriate.
Testing & Debugging: Ability to test application behaviour systematically, investigate failures, and improve the reliability of AI-driven workflows.
Problem-Solving: Strong analytical thinking and the ability to break down ambiguous problems into practical engineering solutions.
Collaboration: Clear communication skills, openness to feedback, and the ability to work effectively with engineers and product stakeholders.
Production LLM Applications: Experience building and deploying LLM-powered applications beyond simple prompt-based prototypes.
Agentic Systems: Hands-on experimentation with AI agents, tool calling, function calling, or multi-step reasoning workflows.
AI Frameworks: Familiarity with frameworks and libraries such as LangChain, LangGraph, LlamaIndex, or equivalent tools.
Vector Databases & Search: Experience with vector databases or search technologies such as pgvector, Pinecone, Weaviate, or Elasticsearch.
Evaluation & Observability: Exposure to LLM evaluation frameworks, tracing, monitoring, and tools for debugging AI workflows.
Cloud & Deployment: Familiarity with Docker, cloud platforms, deployment workflows, or serving AI applications in production.
Document Intelligence: Experience extracting information from PDFs, long-form documents, or complex business records.
B2B SaaS & Enterprise AI: Experience building AI capabilities for business users, particularly where accuracy, data security, and reliability matter.
AEC & Revenue Operations: Interest in applying AI to proposals, project information, client relationships, marketing, and revenue operations in the AEC industry.