Data Scientist

Kantiv (formerly Joist AI); United States; Remote; USD 110,000 - 150,000; Full time

Job summary

Kantiv is hiring a Data Scientist to design and build AI-powered features for our AI platform, collaborating with backend and AI engineers. You will work with both structured and unstructured data to derive insights, develop agentic solutions, and improve product intelligence.

Ideal candidates have 1–3 years in AI/ML roles, strong Python skills, and practical experience with LLMs, RAG, embeddings, and API integrations.

Qualifications

• 1–3 years of experience in AI/ML engineering, software engineering, or related field.
• Proficiency in Python and writing clean, maintainable code.
• Understanding of LLMs, prompting, context windows, token limits, and structured outputs.
• Familiarity with embeddings, vector search, document retrieval, and building RAG-based apps.
• Experience with REST APIs, JSON, and backend integrations.
• Ability to work with structured and unstructured data using Pandas and SQL.

Responsibilities

• AI Application Development: Build and improve AI-powered applications using LLMs and modern AI APIs.
• LLM Integration: Integrate language models, experiment with prompting strategies and structured outputs.
• RAG & Knowledge Systems: Develop pipelines to retrieve and reason over documents and data sources.
• AI Agents & Workflows: Build AI agents and multi-step workflows with tool calling and controls.
• Data Processing & Retrieval: Ingest documents, extract text, chunk, create embeddings, manage metadata, enable semantic search.
• Evaluation & Reliability: Assess outputs for accuracy and reliability; create test data and evaluation workflows.
• Backend Integration: Work with backend engineers to integrate AI features into APIs and product workflows.
• Performance & Cost Optimisation: Tune models, caching, and retrieval strategies to reduce latency and cost.
• Monitoring & Debugging: Troubleshoot AI pipelines and monitor quality of AI features.
• Experimentation & Collaboration: Stay current with AI techniques and translate customer problems into practical solutions.

Skills

Python

LLM fundamentals

RAG & embeddings

APIs & backend

Data handling

Testing & debugging

Problem solving

Collaboration

Pandas & SQL

Tools

LangChain

LlamaIndex

Vector databases

pgvector

Docker

Job description

About the company

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.

About the role

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.

What you’ll do

• 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.

What you’ll bring

• 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.

Experience we’d be particularly excited about

• 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.