Azure AI Engineer

Company Overview

NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group

Job Summary

The AI Engineer designs, builds, deploys, and maintains AI models and systems that enable scalable business solutions. Leveraging cloud platforms such as Microsoft Azure, the role develops production-ready AI pipelines and integrates intelligent capabilities into enterprise applications.

Working closely with Data Engineers, Software Engineers, and Product Teams, the AI Engineer ensures efficient data flow, reliable model deployment, and continuous performance optimization. The role is responsible for improving model accuracy, scalability, and reliability in real-world environments.

The AI Engineer also upholds best practices in data quality, security, governance, and Responsible AI, ensuring solutions are ethical, compliant, and aligned with organizational and regulatory standards.

Key Responsibilities

• Design, build, and deploy AI models and end-to-end AI pipelines for production environments

• Integrate AI capabilities into applications and services using APIs and cloud-native architectures

• Collaborate with Data Engineers, Software Engineers, and Product Teams to ensure seamless data flow and system integration

• Monitor, evaluate, and optimize model performance, accuracy, and scalability in real-world use

• Develop and manage Prompt Flows, orchestration pipelines, and agent-based AI workflows

• Implement Retrieval-Augmented Generation (RAG) solutions, including embedding, indexing, and context management

• Ensure adherence to Responsible AI practices, including model safety, governance, and compliance standards

• Establish observability, logging, and performance monitoring for AI systems

• Apply secure-by-design principles, including identity management, access control, and data protection

• Translate business requirements into AI solutions, defining guardrails, KPIs, and success criteria

Experience Required

• Related Work Experience - 3–6+ years in AI/ML, software engineering, or cloud-based AI solution development

  • Hands-on experience building and deploying AI/LLM-powered applications in production

  • Experience with Azure or similar cloud platforms (AWS/GCP)

  • Proven work on prompt engineering, orchestration, or RAG-based solutions

  • Experience collaborating in cross-functional product or engineering teams

  • Minimum 3-5 years of experience with Azure AI services

• Knowledge – knowledgeable in the following:

  • AI/LLM engineering: prompt design, orchestration (Prompt Flow), agent-based systems, and RAG implementation

  • Software engineering: Python, APIs (REST/JSON), microservices, and CI/CD practices

  • Azure AI ecosystem: AI Foundry, model deployment, inference APIs, and cost optimization

  • Data and search: embeddings, chunking strategies, and Azure AI Search (hybrid retrieval)

  • Cloud and security: Azure networking, identity (Entra ID), observability, and secure-by-design architectures

  • Responsible AI: model safety, governance, explainability, and policy enforcement

  • Low-code integration: Copilot Studio and Power Platform extensibility

• Skills

  • Translates business problems into practical AI solutions

  • Communicates complex, probabilistic outputs to non-technical stakeholders

  • Strong analytical thinking and problem-solving in ambiguous environments

  • Effective collaboration across engineering, data, and product teams

  • Ability to define guardrails, KPIs, and success criteria for AI solutions

Bachelor's degree in a relevant field is required.

Azure certifications are preferred but not required at the time of application.