About The Role
We are looking for an AI Engineer (Data) to help build and scale AI-powered solutions that enhance how our teams access, analyze, and utilize data. As part of the Data team, you will develop agentic applications and AI-driven tools that leverage Large Language Models (LLMs) to improve internal workflows, enable self-service analytics, and increase the impact of data across the organization.
In addition to building AI solutions, you will work closely with data analysts and engineers to design data architectures, prototype innovative use cases, and transform large-scale data into actionable insights that support strategic decision-making.
What You Will Do
Design, develop, and maintain AI agents that enable non-technical stakeholders to interact with data through natural language.
Build AI-powered knowledge retrieval and search systems using modern LLM technologies.
Develop internal AI tools, reusable agent components, MCP servers, workflow automations, and AI-assisted development utilities to improve team productivity.
Architect and optimize data structures and data warehouse access patterns to support efficient and reliable AI agent interactions.
Collaborate with data analysts and engineers to prototype, evaluate, and productionize AI-driven solutions.
Implement retrieval, context management, tool-calling, and multi-step reasoning workflows for agentic applications.
Establish evaluation, monitoring, and observability frameworks to measure the quality, reliability, and business impact of AI systems.
Partner with the Head of Data & AI to define success metrics and continuously improve AI products based on adoption, accuracy, efficiency, and business outcomes.
What We Are Looking For
Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related field.
3-5 years of experience in Software Engineering, Machine Learning Engineering, Data Engineering, or a related discipline.
Hands-on experience building AI applications using LLMs, including agentic workflows, RAG systems, tool/function calling, context management, or autonomous agents.
Strong SQL skills and experience working with data warehouses, data pipelines, and modern data platforms.
Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, Haystack, Google ADK, or similar technologies.
Familiarity with modern LLM capabilities and trade-offs, including prompting, model selection, latency, cost optimization, reliability, and evaluation.
Experience implementing evaluation and monitoring frameworks for AI applications (e.g., RAGAS, LLM-as-a-Judge, automated quality assessments).
Strong communication and stakeholder management skills, with the ability to explain technical concepts to non-technical audiences.
Nice To Have
Experience designing data architectures that support analytics, internal tooling, and AI-driven applications.
Familiarity with agentic protocols such as Model Context Protocol (MCP).
Experience with BigQuery, Tableau, dbt, Airflow, or similar data platforms and orchestration tools.
Knowledge of AI governance, security, and risk management, including PII protection, prompt injection mitigation, and model evaluation.
AI-native development mindset and experience using modern AI coding tools.
Experience building systems that integrate LLMs with structured and unstructured data workflows.
Professional working proficiency in English.
Experience in fintech, payments, blockchain, or cryptocurrency industries.