AI Engineer (Azure)
· LLMs and agentic AI: Strong hands-on experience with LLMs, tool or function calling, and multi-agent orchestration, including designing and integrating tool engines.
· Security and governance: A solid understanding of AI security and governance, covering data protection, threat modelling, prompt injection defense, and content safety, such as Azure AI Content Safety and Prompt Shields.
· Azure platform: Approx. competencies required:
o Azure Databricks (Spark/Delta/ML runtime): data prep pipelines, feature engineering, ML training workflows, job clusters and MLflow usage.
o Azure Machine Learning: model packaging, managed online endpoints, deployments (blue/green or canary), monitoring basics and registry practices.
o Azure OpenAI: prompt design for controlled recommendations, safe/consistent response patterns, integration patterns and content safety considerations.
o API layer: Azure API Management policies (OAuth2/client credentials, rate limiting, routing), plus REST API design and integration with Salesforce callouts.
o Security & Ops: Key Vault, managed identities/RBAC, logging/telemetry (App Insights/Log Analytics) and CI/CD (Azure DevOps pipelines) for retraining + deployment.
· Cost management: A good understanding of cost governance, including budgets and alerts, tracking token usage per request, and thread or conversation management.
· RAG and data: Experience building Retrieval-Augmented Generation (RAG) pipelines, including chunking, embeddings, vector search (Azure AI Search or pgvector) and grounding strategies.
· Evaluation and quality: Experience evaluating LLM output for groundedness, relevance and hallucination, and running regression tests on prompts using tools such as Azure AI Foundry Evaluations.
· Observability: Knowledge of end-to-end tracing and logging with Application Insights and OpenTelemetry, covering prompts, responses, latency and failures.
· Frameworks: Hands-on experience with agent frameworks and APIs such as Semantic Kernel, LangGraph, the Azure AI Agent Service, FastAPI and MCP (Model Context Protocol).
· Identity and access: Understanding of Entra ID, Managed Identity, RBAC, Key Vault and private networking (VNet and private endpoints).
· DevOps and LLMOps: Experience with CI/CD (Azure DevOps or GitHub Actions), infrastructure as code (Bicep or Terraform), and prompt and model version management.
· Responsible AI: Awareness of Responsible AI principles, human-in-the-loop design, auditability, and compliance with company AI guidelines.
· Enterprise integration: Experience integrating AI solutions with enterprise systems such as Salesforce, SAP and middleware, through APIs or events.
· Soft skills: Strong problem-solving and communication skills, with the ability to explain AI design decisions to architects and business stakeholders
Pay: RM10,000.00 - RM16,000.00 per month
Experience:
- Azure AI Engineer: 10 years (Required)
- LLM: 7 years (Required)
- Enterprise Integration: 7 years (Required)
Language:
- English (Required)
Work Location: In person
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