Forward Deployed Engineer

AIQURIS; Singapore; On-site; SGD 140,000 - 230,000; Full time

Job summary

AIQURIS is seeking a Forward Deployed Engineer to design and deploy production-grade multi-agent systems in regulated enterprise environments. You will build scalable AI applications, RAG pipelines, and orchestration frameworks, collaborating with Architects, Delivery teams, and client stakeholders.

The role demands hands-on engineering, production delivery experience, and deep enterprise AI practices. You will implement guardrails, deploy on AWS services, develop FastAPI services and React

Qualifications

• 4–6 years in software or AI engineering
• Strong Python and production backend experience
• Hands-on with LLM APIs, RAG, and orchestration
• Experience with enterprise AWS deployments

Responsibilities

• Design and deliver production-grade multi-agent systems
• Build end-to-end RAG pipelines and retrieval optimization
• Develop prompt frameworks and guardrails for enterprise AI
• Deploy AI systems in in-region and VPC environments
• Build FastAPI services and enterprise integrations
• Lead technical workstreams and mentor juniors

Skills

Python

Backend engineering

AI engineering

DevSecOps

Problem-solving

Client-facing

Tools

LangGraph

LangChain

CrewAI

FastAPI

React

AWS (Bedrock/SageMaker/ECS/EKS)

Job description

About the role

The Forward Deployed Engineer designs, builds, and deploys production-grade multi-agent systems within regulated enterprise environments. This role is responsible for developing scalable AI applications, retrieval pipelines, orchestration frameworks, APIs, observability tooling, and production infrastructure while working closely with Architects, Delivery teams, and enterprise client stakeholders. The role requires strong hands-on engineering capability, production delivery experience, and a strong understanding of enterprise AI engineering practices. All engineers use AI tools effectively in their daily work. AI assistants and workflow automation tools are expected to improve engineering productivity, testing, debugging, documentation, and delivery execution. What You Will Do Design and deliver production-grade multi-agent systems using orchestration frameworks such as LangGraph, LangChain, CrewAI, or equivalent technologies. Build and maintain end-to-end RAG pipelines including chunking strategies, embedding evaluation, retrieval optimization, reranking, and hallucination evaluation. Develop prompt frameworks, role instructions, escalation flows, and guardrail mechanisms for enterprise AI applications. Implement production-grade AI guardrails including PII redaction, input sanitization, output validation, adversarial testing, and security controls. Design and deploy AI systems within in-region and in-VPC enterprise environments using AWS services and regulated deployment architectures. Build and maintain FastAPI services, streaming APIs, authentication layers, React-based interfaces, and enterprise integrations. Deploy and manage workloads on ECS, EKS, SageMaker, Bedrock, API Gateway, and related AWS infrastructure services. Implement CI/CD pipelines, automated testing workflows, infrastructure automation, observability tooling, and production monitoring frameworks. Define and execute testing strategies including unit testing, integration testing, retrieval testing, adversarial testing, and evaluation frameworks. Contribute to architecture reviews, low-level designs, solution documentation, and technical proposal responses. Lead technical workstreams within engagements and support junior engineering team members through reviews, debugging, and technical guidance. Contribute reusable engineering assets including runbooks, prompt libraries, evaluation frameworks, deployment templates, and tooling accelerators.

What you'll bring

4 - 6 years of experience in software engineering, AI engineering, cloud engineering, or backend platform development. Strong Python engineering capability with experience building production-grade backend systems. Hands-on experience building AI applications using LLM APIs, RAG systems, orchestration frameworks, or multi-agent workflows. Experience with LangGraph, LangChain, CrewAI, or similar agentic AI orchestration frameworks. Strong understanding of retrieval systems, embeddings, vector databases, reranking, chunking strategies, and AI evaluation methodologies. Experience deploying applications on AWS using services such as Bedrock, SageMaker, ECS/EKS, API Gateway, Cognito, CloudWatch, IAM, Secrets Manager, and related services. Strong understanding of DevSecOps practices including CI/CD, infrastructure automation, testing frameworks, observability, and security hygiene. Familiarity with React, Node.js, streaming APIs, and modern web application integration patterns. Understanding of enterprise AI risks including hallucinations, prompt injection, data leakage, and operational guardrails. Ability to work directly with client stakeholders, delivery teams, and architecture teams in enterprise delivery environments. Strong debugging, problem-solving, and engineering ownership capability.