Senior Data & AI Engineer
Posted Updated 1
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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
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