Azure AI Engineer

Career Connect; Manila; On-site; PHP 900,000 - 1,500,000; Full time

Job summary

Career Connect in Manila, Philippines is seeking an experienced AI Engineer to design, build, deploy, and maintain scalable AI models and production pipelines. You will work with Data Engineers, Software Engineers, and Product Teams to ensure robust data flow and reliable deployments.

The role emphasizes responsible AI, governance, and secure-by-design practices, with emphasis on observability and performance optimization in real-world environments.

Qualifications

• 3–6+ years in AI/ML, software engineering, or cloud-based AI.
• 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.
• Knowledge of Python, APIs (REST/JSON), microservices, and CI/CD practices.
• Familiarity with Azure AI ecosystem: AI Foundry, model deployment, inference APIs, and cost.

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.
• Monitor, evaluate, and optimize model performance, accuracy, and scalability.
• 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 Responsible AI practices, including governance, safety, and compliance.
• Establish observability, logging, and performance monitoring for AI systems.
• Apply secure-by-design principles, including identity management and data protection.
• Translate business requirements into AI solutions with guardrails and KPIs.

Skills

AI/ML engineering

Cloud platforms (Azure)

Prompt engineering

MLOps / CI/CD

Python

REST APIs

Cross-functional collaboration

Tools

Azure AI Foundry

Inference APIs

CI/CD tooling

Python tooling

Job description

Job Description

• 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.
• 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.

Requirements

• Related Work Experience - 3–6+ years in AI/ML, software engineering, or cloud-based AI
• a. Hands-on experience building and deploying AI/LLM-powered applications in production
• b. Experience with Azure or similar cloud platforms (AWS/GCP)
• c. Proven work on prompt engineering, orchestration, or RAG-based solutions
• d. Experience collaborating in cross-functional product or engineering teams
• Knowledge – knowledgeable in the following:
• and RAG implementation
• b. c. Software engineering: Python, APIs (REST/JSON), microservices, and CI/CD practices
• Azure AI ecosystem: AI Foundry, model deployment, inference APIs, and cost