AI role · Open offer

LLM Engineer — banking copilots

DBS Bank

Full-time
Senior
GenAI platform
Hybrid
12 Marina Boulevard, Singapore, SG 018982, Singapore · Hybrid
Singapore, Singapore
SGD 120k–180k/year + bonus

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills2 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 12 Marina Boulevard, Singapore, SG 018982, Singapore · Hybrid
Compensation: SGD 120k–180k/year + bonus
Experience level: Senior
Team: GenAI platform
Benefits & perks

About this role

What the team is looking for and how this position fits.

DBS Bank is hiring a Senior LLM Engineer — banking copilots on the GenAI platform team (Hybrid · Singapore, Singapore). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

DBS Bank is a leading APAC digital bank pioneering regulated GenAI copilots from Marina Bay.

LLM Engineer: RAG for relationship managers with audit trails and red-team evals.

Missions

Policy-aware assistants on internal knowledge with PII controls.
Offline eval suites before model/prompt promotion.

Stack & environment

Python, LangChain, Kubernetes, AWS.

What we look for

Regulated industry LLM; eligible to work in Singapore.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
NLP

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Regulated RAG copilot with audit trails and red-team evals.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, NLP, MLOps, and Kubernetes.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Singapore, Singapore)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: SGD 120k–180k/year + bonus, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Regulated RAG copilot with audit trails and red-team evals.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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