- Location
- Singapore
- Workplace
- Hybrid
- Type
- Full-time
- Department
- Engineering
- Experience
- 2+ years
- Closing date
- Today
- Source
- CareersPage
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.