LLM Engineer / GenAI Application Engineer

Newbridge; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Newbridge is expanding its Data & AI practice in Singapore with a hands-on GenAI Engineer role. You’ll work under the Lead AI Architect to turn designs into production, govern, and operate enterprise AI solutions.

You’ll join a senior team and collaborate with global Data & AI and Controls groups, delivering secure, observable GenAI applications and agentic systems with end-to-end ownership.

Qualifications

• 2+ years hands-on shipping production GenAI systems.
• Experience with building RAG pipelines and knowledge layers.
• Experience with integration to enterprise systems via APIs.

Responsibilities

• Build Production GenAI & Agentic Systems from design to production.
• Own RAG pipelines, embeddings, vector stores, and retrieval strategies.
• Integrate frontier models and platform engineering across cloud providers.
• Ensure security, governance, observability, and CI/CD for AI systems.

Skills

Python

API development

Microservices

Cloud-native engineering

LLM fundamentals

Tools

Pinecone

Weaviate

pgvector

Azure OpenAI

LangGraph

CrewAI

AutoGen

LangChain

Semantic Kernel

Azure AI Search

Bedrock

Vertex AI

Job description

US MNC scaling its Data & AI practice in Singapore is now adding a hands‑on GenAI Engineer to the core build team.

This is a builder role. You will work directly under the Lead AI Architect to take enterprise AI and agentic use‑cases from design to production. If the Architect defines what and why, you own how it gets built, deployed, governed, and operated.

You will be part of a small, senior team in Singapore [AI Architect, AI Engineers, Data Engineer, Data Scientists] and work with global Data & AI and Controls teams, plus client engineering and architecture teams.

What You'll Build

1. Build Production GenAI & Agentic Systems
Build and ship GenAI applications and agent-first systems - from POC to production. This includes multi-agent workflows, tool‑calling agents, orchestration using LangGraph / CrewAI / AutoGen / Microsoft Agent Framework, MCP servers, model gateways, and integration with enterprise systems via APIs and events.

2. Own RAG & Knowledge Layer
Design and implement RAG pipelines - ingestion, chunking, embedding, vector stores, hybrid search, re-ranking, knowledge graphs and semantic layers. Optimize for accuracy, latency, cost, and grounding. Build evaluation harnesses for retrieval quality, hallucination, and answer relevance.

3. Model Integration & Platform Engineering
Integrate frontier and open-weight models - Claude, GPT, Gemini, Llama, Gemma, Phi, Mistral etc. - plus APAC / sovereign models where needed - Qwen, SEA-LION, etc. Work across Azure AI Foundry / AOAI, Bedrock, Vertex AI and handle prompt engineering, structured output, function calling, context management, and guardrails. Manage model routing, fallbacks and cost controls.

4. Ship it Right - Secure, Governed, Observable
Build with security and controls from day zero - prompt injection defense, tool authorization, least‑privilege identity, DLP, human approval gates, audit logging. Implement observability, evals, monitoring, and CI/CD for AI systems. Document architectures and produce evidence for governance / audit.

What We're Looking For

• Experience in software engineering / data / ML engineering with at least 2+ years hands‑on shipping production GenAI systems.
• Strong Python, with experience in API development, microservices, and cloud‑native engineering.
• Proven experience building RAG - vector DBs [Pinecone, Weaviate, pgvector, Azure AI Search etc.], embedding models, and retrieval strategies.
• Hands‑on with at least one agentic framework - LangGraph, CrewAI, AutoGen, LangChain, Semantic Kernel or similar.
• Experience with managed AI platforms - Azure OpenAI / Foundry, AWS Bedrock, GCP Vertex AI.
• Understanding of LLM fundamentals - prompting, tool use, evaluation, latency / cost trade-offs, and context window management.
• Comfortable working in consulting / client‑facing environment - you can translate requirements and demo working software to technical stakeholders.

Strong Advantage If You Have:

• Experience with agent evaluation, guardrails and security patterns for agentic AI.
• Knowledge of MLOps / LLMOps - model registries, experiment tracking, CI/CD, monitoring.
• Data engineering - Databricks / Snowflake, lakehouse patterns, Spark / SQL, knowledge graphs.