# AI Engineer (Managed Services) at AvePoint

> Singapore · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Aavepoint%3A8029829 (read 2026-11-05).

## Facts (observed)

- Company: [AvePoint](https://alderhq.app/companies/avepoint)
- Location text: Singapore; Singapore
- Where it can be done from (inferred): Singapore (evidence: "Location: Singapore; Singapore")
- Posted: 2026-06-26 (132 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://www.avepoint.com/careers/job-detail?gh_jid=8029829
- Skills listed (inferred): React, Kubernetes, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Singapore, n 8): p25 $135k, median $143k, p75 $150k

## Description (the employer's text)

We are looking for a highly skilled AI Engineer specializing in Large Language Models (LLMs) and Agentic AI. You will architect, build, and deploy production-grade LLM applications — from intelligent knowledge bases and RAG systems to autonomous multi-agent workflows. You will work hands-on with open-source Chinese and international LLMs (DeepSeek, Qwen, Kimi, etc), implementing everything from model deployment and inference optimization to prompt engineering and agent orchestration. This is a builder role for someone who thrives at the intersection of research and engineering.

### KEY RESPONSIBILITIES

### LLM Application Development

- Design and develop enterprise LLM-powered applications: intelligent Q&A systems, enterprise knowledge base assistants, AI copilots, document analysis tools, and automated customer service agents.
- Architect and implement end-to-end RAG (Retrieval-Augmented Generation) systems: document parsing and chunking (recursive, semantic, agentic), embedding generation (BGE, M3E, GTE), vector retrieval (dense + sparse hybrid search), reranking (bge-reranker, Cohere Rerank), and response synthesis with source attribution.
- Develop and optimize Prompt Engineering strategies: chain-of-thought, tree-of-thought, few-shot prompting, structured output parsing (JSON mode / Pydantic), prompt templates (LangChain/LangSmith), and prompt version management.
- Knowledge in harness engineering, context management in ensuring LLM interactions and or AI agents reliable and deterministic.
### AI Agent & Multi-Agent Systems

- Design and build AI Agent systems using ReAct, Plan-and-Execute, Reflection, and multi-agent collaboration patterns.
- Implement Function Calling and tool-use capabilities, enabling agents to interact with external APIs, databases, and enterprise systems.
- Develop multi-agent orchestration using LangGraph, AutoGen, CrewAI, and other agent frameworks to solve complex enterprise tasks through agent collaboration.
- Design MCP (Model Context Protocol) integrations for standardized LLM tool interoperability.
### Open-Source LLM Deployment & Optimization

- Deploy and optimize latest version of open-source Chinese LLMs: DeepSeek, Qwen, and Kimi for on-premise and private cloud environments.
- Implement model inference optimization: quantization (GGUF/llama.cpp, GPTQ, AWQ, AutoAWQ, FP8/INT8), KV Cache optimization, continuous batching (vLLM, TensorRT-LLM, TGI, SGLang), speculative decoding, and tensor parallelism for high-throughput serving.
- Build and maintain model serving infrastructure using vLLM, TensorRT-LLM, Text Generation Inference (TGI), Ollama, Xinference, and SGLang; configure GPU resource scheduling with Kubernetes + GPU operators. AI gateway tools for routing, model tracking and load balancing such as TrueFoundry, Kubeflow, LiteLLM or Ray for heavy deep learning.
### Model Fine-Tuning & Customization

- Implement efficient fine-tuning pipelines using LoRA, QLoRA, DoRA, and full-parameter fine-tuning on proprietary domain-specific datasets.
- Prepare and curate instruction-following datasets, RLHF/RLAIF datasets, and evaluation benchmarks for domain adaptation.
- Evaluate fine-tuned models using automated benchmarks and LLM-as-a-Judge methodologies.
### Evaluation & Production Operations

- Build and maintain LLM evaluation frameworks: LLM-as-a-Judge, RAGAS, DeepEval, ARES, and custom task-specific metrics for continuous quality monitoring.
- Implement production monitoring for LLM systems: output quality tracking, latency/throughput metrics, cost monitoring, drift detection, and guardrail compliance.
- Design A/B testing frameworks for model comparison and prompt iteration.
- Implement LLM security guardrails: input/output filtering, PII detection, prompt injection defense, content moderation, and safety alignment.
### Research & Technical Leadership

- Track frontier AI research and evaluate emerging technologies (new model architectures, training techniques, inference methods) for enterprise adoption.
- Contribute to internal knowledge sharing: tech talks, documentation, and best-practice guides on LLM development.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aavepoint%3A8029829 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.