Senior Data & AI Engineer

About Our Client

Our client is an early-stage AI startup building a next-generation structured research data platform for the investment research industry .

The company works with professional investment teams conducting fundamental equity research and has already onboarded its first group of institutional clients, including established hedge funds.

With a lean, highly technical team, they are now looking for an early core engineer to work closely with the founding team and build the platform from 0 to 1. This is a hands-on role with significant ownership across the entire data and AI stack — from data ingestion and document intelligence to LLM applications and analyst-facing query services.

About the Role

You will be responsible for building the core data infrastructure powering the company's AI-driven investment research platform.

This is an end-to-end engineering role spanning data pipelines, document processing, data architecture, LLM applications, backend services, and cloud infrastructure . You will have the opportunity to influence key technical decisions and help establish the engineering foundations as the company scales.

What You'll Do

Data Ingestion & Pipelines

• Build and maintain robust, production-grade pipelines across multiple external data sources.
• Design reliable ingestion workflows with scheduling, monitoring, and failure recovery.

Document Processing

• Build systems to extract and structure information from PDF, HTML, XBRL, and other real-world documents .
• Handle scanned PDFs, complex tables, and multilingual content in both Chinese and English.

Data Architecture

• Design and maintain structured, schema-based data models.
• Build on modern data warehouses and databases such as Snowflake, BigQuery, or PostgreSQL .
• Ensure data structures remain scalable, reliable, and efficient to query.

ETL/ELT Orchestration & Data Quality

• Build production-grade ETL/ELT workflows using Airflow, Prefect, Dagster , or similar technologies.
• Implement data quality checks, alerting, observability, and lineage tracking.

LLM Application Engineering

• Build production applications using OpenAI, Anthropic, and/or open-source LLMs .
• Develop reliable structured-output and Retrieval-Augmented Generation (RAG) workflows.
• Implement practical LLM evaluation, quality monitoring, and reliability mechanisms.

Backend Engineering

• Build high-performance APIs and query services using FastAPI or similar frameworks.
• Develop backend services powering downstream investment research and analyst-facing products.

Cloud Infrastructure

• Build and operate services within AWS or GCP .
• Leverage managed cloud services to maintain a lean and efficient infrastructure.

What We're Looking For

• 5–10 years of production software engineering experience , with a strong track record of building and deploying complete systems.
• Strong proficiency in Python and SQL .
• Strong understanding of data modeling, testing, system reliability, and performance optimization .
• Hands-on experience building production data pipelines using Airflow, Prefect, Dagster , or similar orchestration frameworks.
• Experience designing schemas and data models using Snowflake, BigQuery, PostgreSQL , or similar technologies.
• Hands-on experience processing messy, real-world documents such as PDFs, HTML, XBRL, scanned documents, or complex tables .
• 2+ years of hands-on experience building production LLM applications , ideally including RAG, structured outputs, or other real-world AI features.
• Experience working with AWS or GCP .
• Strong communication skills with the ability to clearly articulate technical designs and trade-offs.
• Professional working proficiency in English.
• Experience as a Founding Engineer, early-stage engineer, or core member of a startup engineering team is highly valued.
• Experience building end-to-end data platforms or infrastructure from 0 to 1 is a strong plus.
• Experience building data infrastructure specifically for NLP or LLM workloads is a plus.
• Experience working with financial, investment research, or other complex document-heavy datasets is advantageous.
• Experience with dbt , multi-tenant system design, or data access control is a plus.
• Familiarity with graph databases such as Neo4j is advantageous.
• Experience with OCR, document intelligence, table extraction, or financial filings/XBRL is a plus.

AIM Global Talent Pte. Ltd. | EA Licence No. 25C3207