LLM Engineer / GenAI Application Engineer

NEWBRIDGE ALLIANCE PTE. LTD.; Singapore; On-site; SGD 110,000 - 170,000; Full time

Job summary

US MNC in Singapore is scaling its Data & AI practice and seeking a hands-on GenAI Engineer to build production GenAI systems under the Lead AI Architect.

You will own how things are built, deployed, governed and operated, collaborating with a small senior team across global Data & AI and Controls groups.

Qualifications

• 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.

Responsibilities

• Build Production GenAI & Agentic Systems from design to production with governance and observability.
• Own RAG pipelines including ingestion, embedding, vector stores, and knowledge layers for accuracy and cost.
• Integrate frontier and open-weight models across cloud platforms and manage prompts, routing, and guardrails.
• Ensure secure, auditable, and observable AI systems with CI/CD and governance artifacts.

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.