AI Application Engineer

Rajah & Tann Asia; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Rajah & Tann Asia seeks a hands-on AI Applications Developer to design, build, integrate, and support enterprise AI-enabled applications, copilots, document intelligence solutions, knowledge search experiences, and automation workflows.

This role emphasizes AI application delivery over Azure infrastructure engineering, translating business requirements into practical AI solutions using LLMs, RAG, prompt engineering, API integration, and secure data governance.

Qualifications

• Bachelor’s degree in CS or related field required.
• 6+ years hands-on app development and delivery experience.
• Experience with AI-enabled apps, GenAI features, copilots, chatbots.

Responsibilities

• Design and support AI-enabled apps, copilots, and knowledge tools.
• Translate business problems into AI use cases and requirements.
• Build RAG solutions with embeddings, vector search, and grounding.
• Develop AI orchestration for prompts, tools, memory, API calls, workflows.
• Design prompts, guardrails, and testing datasets for reliability.
• Develop backend services using .NET/C#, Python, REST APIs.
• Create user-facing AI components and admin/workflow screens.
• Integrate AI apps with enterprise systems and data sources.
• Ensure secure authentication, data protection, and governance.
• Maintain CI/CD, tests, reviews, and deployment practices.

Skills

AI application delivery

GenAI features

Prompt engineering

API integration

Secure data practices

Education

Bachelor’s degree in Computer Science or related field

Tools

.NET/C#

Python

REST APIs

Azure OpenAI

Job description

We are seeking a hands‑on AI Applications Developer to design, build, integrate, and support enterprise AI‑enabled applications, copilots, document intelligence solutions, knowledge search experiences, and automation workflows.

This role is focused on AI application delivery rather than Azure infrastructure engineering. The successful candidate will translate business requirements into practical AI solutions using Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG), prompt engineering, API integration, secure data management, and modern software engineering practices.

The role partners closely with business stakeholders, application teams, security teams, data owners, and external vendors to deliver scalable, governed, secure, and supportable AI solutions that improve productivity, automate business processes, and safeguard sensitive information.

Key Responsibilities

• Design, develop and support AI‑enabled applications, copilots, internal assistants, document Q&A solutions, knowledge search tools and intelligent workflow automation features.
• Translate business problems into AI application use cases, user journeys, functional requirements, acceptance criteria and production‑ready application features.
• Build Retrieval‑Augmented Generation solutions using document ingestion, chunking, embeddings, vector search, semantic search, metadata filtering, grounding, ranking and source citation patterns.
• Develop AI orchestration logic for prompts, tools, memory, retrieval, function calling, API calls, workflow steps, fallback handling and human‑in‑the‑loop review where required.
• Design and maintain prompt templates, system instructions, guardrails, response formats, evaluation criteria and testing datasets to improve accuracy, consistency and reliability.
• Develop backend services, APIs and application logic using .NET/C#, Python, REST APIs and approved enterprise development frameworks.
• Develop user‑facing AI application components such as chat interfaces, review screens, feedback capture, search experiences, admin screens and workflow forms using approved frontend technologies.
• Integrate AI applications with enterprise systems, SQL databases, document repositories, SharePoint/Microsoft 365 content, workflow tools, APIs and secure data sources.
• Implement secure authentication, authorization, role‑based access, data filtering and user‑context‑aware responses for AI applications.
• Ensure confidential data, client data, privileged documents, prompts, completions, logs and embeddings are handled according to internal governance and data protection requirements.
• Build validation and evaluation approaches for AI outputs, including test questions, expected answers, hallucination checks, citation checks, regression testing and user feedback loops.
• Apply secure coding practices, input validation, output handling, error handling, audit logging, dependency control and secure API development.
• Build and maintain CI/CD pipelines, automated tests, code review workflows and deployment practices for AI application releases.
• Monitor and troubleshoot production AI applications, including issues with prompts, retrieval quality, search indexes, APIs, data pipelines, authentication, performance and user experience.
• Maintain documentation including solution notes, prompt documentation, data source mapping, API documentation, deployment guides, runbooks, known limitations and support handover materials.
• Work with security, infrastructure and data teams to ensure AI applications are governed, monitored, supportable and safe for enterprise use.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Engineering or a related field.
• Minimum 6 years of hands‑on experience in application development, software engineering, integration development or enterprise application delivery.
• Experience building or integrating AI‑enabled applications, GenAI features, copilots, chatbots, document intelligence or knowledge search solutions.
• Minimum 4 years of hands‑on experience working with .NET/C#, Python or both, including backend services, APIs, data processing and application integration.
• Experience with large language model application patterns, prompt engineering, RAG, embeddings, vector search, semantic search and response grounding.
• Experience with Azure OpenAI, Azure AI Search, Azure AI Foundry, OpenAI‑compatible APIs or similar enterprise AI platforms.
• Experience working with structured and unstructured data, including documents, PDFs, SharePoint content, SQL data, metadata and business knowledge repositories.
• Experience developing REST APIs, integration services, application workflows and enterprise‑grade backend components.
• Experience with SQL Server, Azure SQL, PostgreSQL or similar relational database platforms.
• Experience with Git, pull requests, code reviews, branching standards, automated testing and CI/CD pipelines.
• Good understanding of secure SDLC, OWASP principles, authentication, authorization, secrets management, dependency scanning and secure API development.
• Good understanding of responsible AI, data governance, confidentiality, auditability, human review and safe use of AI outputs in enterprise environments.