AI /LLM Application Engineer - Kuala Lumpur

Newbridge; Kuala Lumpur; Presencial; MYR 380.000 - 571.000; Jornada completa

Descripción de la vacante

Newbridge is expanding its Data & AI practice with a hands-on GenAI Engineer role in Singapore. You will work under the Lead AI Architect to take enterprise use-cases from design to production, owning how it gets built, deployed, governed and operated.

You’ll join a senior team and collaborate with global Data & AI teams to deliver scalable GenAI solutions, including multi-agent workflows, RAG pipelines, and secure, observable architectures.

Formación

• 2+ years shipping production GenAI systems in software/data/ML roles.
• Strong Python, API development, microservices, cloud-native engineering.
• Hands-on with RAG pipelines and vector DBs.
• Experience with agentic frameworks: LangGraph, CrewAI, AutoGen, LangChain, Semantic Kernel.
• Experience with managed AI platforms: Azure OpenAI Foundry, AWS Bedrock, GCP Vertex AI.
• Understanding of LLM fundamentals: prompting, tool use, evaluation, latency and cost.
• Comfortable in consulting/client-facing environments.

Responsabilidades

• Build Production GenAI & agent-first systems from design to production.
• Own RAG pipelines and knowledge layer implementation.
• Model integration across frontier/open-weight models and platforms.
• Ensure security, governance, observability, and CI/CD for AI systems.

Conocimientos

Python

API development

Microservices

Cloud-native

LLM fundamentals

Prompting

Context management

Herramientas

Pinecone

Weaviate

pgvector

Azure AI Search

LangGraph

CrewAI

AutoGen

LangChain

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.