AI Engineer (FDE)

TECHKNOWLEDGEY PTE. LTD.; Singapore; On-site; SGD 85,000 - 115,000; Full time

Benefits offered by this job

Competitive compensation

Exposure to enterprise-scale AI projects

Job summary

TECHKNOWLEDGEY PTE. LTD. is seeking a Forward Deployed Engineer in Singapore to design and build multi-agent systems within regulated environments. You'll work collaboratively with architects and stakeholders to deliver scalable AI applications.

The role requires strong production delivery experience, particularly with AWS architectures, and hands-on engineering experience. Ideal candidates will have a background in AI engineering and will utilize AI tools to enhance productivity.

Qualifications

• 4–6 years in software engineering, AI engineering, or cloud engineering.
• Strong understanding of retrieval systems and embeddings.
• Capability to engage with client stakeholders.

Responsibilities

• Design and deliver production-grade multi-agent systems.
• Build and maintain end-to-end RAG pipelines.
• Implement production-grade AI guardrails.

Skills

Strong Python engineering

Experience with LLM APIs

AWS deployment experience

Strong DevSecOps understanding

Debugging and problem-solving

Tools

LangGraph

LangChain

CrewAI

FastAPI

React

Job description

About the Company

Our client is a specialist systems integrator building agentic AI systems for major banks, telcos, insurers, and retailers across Asia Pacific. They work on high-impact problems for institutions running critical financial and digital infrastructure in the region.

They operate exclusively on AWS and benefit from strategic partnership programmes that provide co-sell reach, funded innovation opportunities, and delivery credibility across multiple markets. They value engineers who are technically strong, delivery-focused, collaborative, and committed to solving complex problems.

The Role

This Forward Deployed Engineer designs, builds, and deploys production-grade multi-agent systems within regulated enterprise environments — working closely with Architects, Delivery teams, and enterprise client stakeholders on scalable AI applications, retrieval pipelines, orchestration frameworks, APIs, observability tooling, and production infrastructure.

The role calls for strong hands-on engineering, production delivery experience, and solid enterprise AI engineering practices. All engineers are expected to use AI tools effectively to boost productivity across testing, debugging, documentation, and delivery.

What You Will Do

• Design and deliver production-grade multi-agent systems using LangGraph, LangChain, CrewAI, or equivalent orchestration frameworks
• Build and maintain end-to-end RAG pipelines (chunking, embedding evaluation, retrieval optimization, reranking, hallucination evaluation)
• Develop prompt frameworks, role instructions, escalation flows, and guardrail mechanisms
• Implement production-grade AI guardrails: PII redaction, input sanitization, output validation, adversarial testing, security controls
• Design and deploy AI systems in-region/in‑VPC using regulated AWS architectures
• Build and maintain FastAPI services, streaming APIs, auth layers, React interfaces, and enterprise integrations
• Deploy/manage workloads on ECS, EKS, SageMaker, Bedrock, API Gateway
• Implement CI/CD, automated testing, infrastructure automation, observability, and monitoring
• Define/execute testing strategies (unit, integration, retrieval, adversarial, evaluation)
• Contribute to architecture reviews, low-level designs, solution docs, and proposal responses
• Lead technical workstreams and mentor junior engineers
• Build reusable engineering assets: runbooks, prompt libraries, evaluation frameworks, deployment templates

What They're Looking For

• 4–6 years in software engineering, AI engineering, cloud engineering, or backend platform development
• Strong Python engineering for production-grade backend systems
• Hands‑on experience with LLM APIs, RAG systems, orchestration frameworks, or multi‑agent workflows
• Experience with LangGraph, LangChain, CrewAI, or similar
• Strong grasp of retrieval systems, embeddings, vector databases, reranking, chunking, AI evaluation
• AWS deployment experience: Bedrock, SageMaker, ECS/EKS, API Gateway, Cognito, CloudWatch, IAM, Secrets Manager
• Strong DevSecOps understanding: CI/CD, infrastructure automation, testing, observability, security hygiene
• Familiarity with React, Node.js, streaming APIs, modern web integration patterns
• Understanding of enterprise AI risks: hallucinations, prompt injection, data leakage, guardrails
• Ability to engage directly with client stakeholders and architecture teams
• Strong debugging, problem-solving, ownership mentality

What Sets You Apart

• Delivered production AI systems used by real users/enterprises
• Active, effective use of AI tools in your own workflow
• Comfortable troubleshooting production issues, improving reliability
• Track record of building reusable tooling/frameworks adopted by teams
• Strong engineering execution paired with stakeholder communication

Certifications (valued, not required)

AWS Certified Developer Associate, AWS Certified Solutions Architect Associate, or AWS Certified Machine Learning Engineer Associate

What's on Offer

Competitive, market‑aligned compensation. Exposure to enterprise‑scale AI transformation programmes across APAC, advanced AI tooling, AWS partnership ecosystems, and regulated‑industry delivery experience. Equal opportunity employer.