LLM Engineer GenAI Application Engineer

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 Youll 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 Were 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.