Senior AI Data Scientist

Kerry Consulting; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Kerry Consulting is seeking an experienced Senior AI Data Scientist to join a leading investment management firm in Singapore. The role focuses on hands-on development and deployment of advanced AI solutions across investment and business platforms.

You will drive end-to-end LLM and agentic AI projects, collaborating with investment professionals and technology teams to transform research, analysis, and decision-making workflows, and ensure production readiness.

Qualifications

• Senior, hands-on AI Data Scientist with end-to-end LLM and Generative AI production experience.
• Strong Python and ML fundamentals with practical experience across LLMs, RAG, embeddings, vector databases, tool calling, evaluation, and agent orchestration.
• Hands-on experience building agentic or multi-agent workflows using LangGraph, LangChain, AutoGen, CrewAI or equivalents.
• Experience with cloud AI platforms, MLOps/LLMOps, APIs, CI/CD, model monitoring, and production software engineering practices.
• Experience within investment management, asset management, hedge funds, investment banking, or broader financial markets would be advantageous.

Responsibilities

• Design, develop, and deploy end-to-end LLM and agentic AI solutions.
• Build agentic workflows and multi-agent systems across investment workflows.
• Work with structured and unstructured financial data and integrate with enterprise data/applications.
• Establish evaluation and monitoring frameworks to ensure accuracy, reliability, security, production readiness.

Skills

Python

LLM

Generative AI

RAG

embeddings

vector databases

tool calling

evaluation

agent orchestration

LangGraph

LangChain

AutoGen

CrewAI

cloud AI platforms

MLOps/LLMOps

APIs

CI/CD

model monitoring

production software engineering

Tools

LangGraph

LangChain

AutoGen

CrewAI

Job description

We are seeking an experienced Senior AI Data Scientist to join a leading investment management firm, developing and deploying advanced AI solutions across the investment and broader business platform.

This is a hands‑on role focused on taking LLM and agentic AI solutions from concept through to production, working closely with investment professionals, data teams, and technology stakeholders to transform research, analysis, and decision‑making workflows.

Responsibilities

You will design, develop, and deploy end‑to‑end LLM and agentic AI solutions, covering data preparation, model and architecture selection, prompt engineering, RAG, evaluation, deployment, monitoring, and continuous improvement.

You will build agentic workflows and multi‑agent systems capable of reasoning, retrieving information, using tools and APIs, and executing multi‑step tasks across investment workflows. Use cases may include investment research, market intelligence, document analysis, portfolio analytics, risk, and investment operations.

You will work with structured and unstructured financial data, integrate AI solutions with enterprise data and applications, and establish robust evaluation and monitoring frameworks to ensure solutions are accurate, reliable, secure, and production‑ready.

Requirements

We are looking for a senior, hands‑on AI Data Scientist with strong experience delivering LLM and Generative AI solutions end‑to‑end in production. You should have strong Python and machine learning fundamentals, together with practical experience across LLMs, RAG, embeddings, vector databases, tool/function calling, evaluation, and agent orchestration.

Hands‑on experience building agentic or multi‑agent workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent technologies is highly desirable. Experience with cloud AI platforms, MLOps/LLMOps, APIs, CI/CD, model monitoring, and production software engineering practices will also be important.

Experience within investment management, asset management, hedge funds, investment banking, or broader financial markets would be advantageous. The successful candidate should be able to combine strong technical depth with an understanding of investment workflows and communicate effectively with investment and business stakeholders.