AI /LLM Application Engineer - Kuala Lumpur

Newbridge Alliance Pte Ltd; Kuala Lumpur; Presencial; MYR 285.000 - 475.000; Jornada completa

Descripción de la vacante

Newbridge Alliance Pte Ltd is seeking a hands-on GenAI Engineer to build production GenAI and agentic systems under the AI Architect. You will own how it is built, deployed, governed, and operated within enterprise contexts.

You will collaborate with a senior team across Data & AI, Controls, and client engineering groups to deliver secure, observable AI platforms with measured retrieval quality and cost controls.

Formación

• Experience shipping production GenAI systems in software/data/ML engineering roles.
• Proficiency in Python and API development for cloud-native environments.
• Hands-on with RAG, vector stores and retrieval strategies.

Responsabilidades

• Build GenAI applications from design to production, including agentic workflows.
• Own integration, governance, observability and deployment pipelines for AI systems.
• Collaborate with cross-functional teams to deliver secure and scalable AI solutions.

Conocimientos

Python

APIs

Microservices

Cloud-native

RAG pipelines

Vector databases

LangGraph

CrewAI

AutoGen

LangChain

Azure OpenAI

Bedrock

Vertex AI

LLM fundamentals

Consulting

Herramientas

Pinecone

Weaviate

pgvector

Azure AI Search

Descripción del empleo

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.