Azure AI Engineer

Create Synergies Inc.; Taguig; On-site; PHP 1,200,000 - 2,400,000; Full time

Job summary

Create Synergies Inc. seeks an Azure AI Engineer to design, deploy, and support enterprise-grade AI solutions on Microsoft Azure, focusing on scalable, production-ready models and responsible AI governance.

You will collaborate with cross-functional teams to translate business requirements into robust AI apps, implement RAG and embedding strategies, ensure observability, security, and cost optimization across cloud architectures.

Qualifications

• At least 3–6 years in AI/ML engineering or cloud-based AI solution development.
• Hands-on production experience with AI/LLM apps on cloud platforms.
• Proficiency in Azure services and prompt engineering, orchestration, or RAG.

Responsibilities

• Design, develop, and deploy production-ready AI models and end-to-end AI solutions.
• Build and integrate AI capabilities into enterprise applications using APIs and cloud architectures.
• Partner with software, data, and product teams to deliver scalable AI solutions.
• Monitor and improve model performance, reliability, and scalability in production.
• Develop and maintain prompt orchestration workflows and AI pipelines.
• Implement RAG architectures with embedding generation and context retrieval.
• Promote Responsible AI with governance, security, and ethical standards.
• Establish monitoring, logging, and observability for AI applications.
• Apply secure-by-design principles including IAM and data protection.
• Translate business requirements into measurable success metrics and guardrails.

Skills

Azure AI engineering

Python

REST APIs

CI/CD

Cross-functional collaboration

Tools

Azure

AWS

LLMs

Job description

Job Overview

Our client is seeking an Azure AI Engineer to design, develop, deploy, and support enterprise‑grade AI solutions that address business challenges through modern cloud technologies. This role focuses on building scalable AI applications, integrating intelligent capabilities into existing platforms, and ensuring reliable deployment in production environments. Working closely with cross‑functional engineering, data, and product teams, the successful candidate will help deliver robust AI solutions while maintaining high standards for performance, security, governance, and Responsible AI practices.

Key Responsibilities

• Design, develop, and deploy production‑ready AI models and end‑to‑end AI solutions.
• Build and integrate AI capabilities into enterprise applications using APIs and cloud‑based architectures.
• Partner with software engineers, data engineers, and product teams to deliver scalable and well‑integrated AI solutions.
• Monitor, evaluate, and continuously improve model performance, reliability, scalability, and accuracy in production.
• Develop and maintain prompt orchestration workflows, AI pipelines, and agent‑based solutions.
• Implement Retrieval‑Augmented Generation (RAG) architectures, including embedding generation, indexing strategies, and context retrieval.
• Promote Responsible AI practices by ensuring compliance with governance, security, and ethical AI standards.
• Establish monitoring, logging, and observability processes for AI applications to support operational excellence.
• Apply secure‑by‑design principles, including identity management, access controls, and data protection measures.
• Translate business requirements into practical AI solutions by defining success metrics, performance indicators, and operational guardrails.

Qualifications

Experience

• 3–6+ years of professional experience in AI/ML engineering, software engineering, or cloud‑based AI solution development.
• Hands‑on experience building, deploying, and supporting AI or large language model (LLM) applications in production environments.
• Experience working with Microsoft Azure or comparable cloud platforms such as AWS or Google Cloud Platform.
• Practical experience in prompt engineering, AI orchestration, or Retrieval‑Augmented Generation (RAG) implementations.
• Experience collaborating with cross‑functional engineering and product teams throughout the software development lifecycle.

Technical Knowledge

• AI and LLM engineering, including prompt engineering, workflow orchestration, agent‑based AI, and RAG solutions.
• Software development using Python, REST APIs, JSON, microservices, and CI/CD methodologies.
• Azure AI services, including model deployment, inference APIs, AI development platforms, and cloud cost optimization.
• Data retrieval techniques such as embeddings, chunking strategies, vector search, and hybrid search implementations.
• Cloud infrastructure, identity and access management, observability, networking, and secure cloud architectures.
• Responsible AI principles, including model governance, explainability, policy compliance, and AI safety.
• Low‑code AI integration using Microsoft Power Platform and Copilot‑based extensibility.

Skills

• Ability to translate business needs into scalable AI‑driven solutions.
• Strong communication skills with the ability to explain complex AI concepts to both technical and non‑technical stakeholders.
• Excellent analytical thinking and problem‑solving skills, particularly in complex or evolving environments.
• Effective collaboration across engineering, product, and data teams.
• Ability to establish measurable success criteria, performance metrics, and governance controls for AI solutions.