Azure AI Engineer

ARISTON SERVICES PTE. LTD.; Singapore; In loco; SGD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

ARISTON SERVICES PTE. LTD. in Singapore seeks an experienced Azure AI Engineer to design, deploy and operationalize enterprise GenAI solutions on Microsoft Azure, enabling scalable AI workloads across the enterprise.

You will work on end-to-end GenAI projects, build RAG pipelines with Azure OpenAI and Azure Search, and implement MLOps practices using Azure ML, DevOps, and containerized deployments. Strong focus on security, governance and cost efficiency.

Competenze

• 5+ years of technical experience, with 2–3 years in AI/ML on Azure.
• Hands-on experience with Azure AI Foundry, OpenAI Service, and Azure ML.
• Experience building GenAI, RAG pipelines, and LLM orchestration.
• Familiarity with MLOps/LLMOps, security and governance on Azure.

Mansioni

• Architect end-to-end GenAI apps using Azure OpenAI Service.
• Build RAG architectures with Azure AI Search and vector stores.
• Orchestrate LLM workflows with Semantic Kernel, LangChain or LlamaIndex.
• Manage model lifecycle and deployment via Azure ML and MLflow.
• Implement CI/CD for AI models with Azure DevOps or GitHub Actions.

Conoscenze

Azure AI Foundry

Azure OpenAI Service

Azure Machine Learning

Azure AI Search

Python

LLM orchestration

Semantic Kernel

LangChain

LlamaIndex

Pytorch

Hugging Face

SQL

DevOps on Azure

Docker

Terraform

Formazione

Microsoft Certified: Azure AI Engineer Associate (AI-102)

Microsoft Certified: Azure Data Scientist Associate (DP-100)

Strumenti

MLflow

Docker

AKS

Azure Container Apps

Descrizione del lavoro

Job Description: Azure AI Engineer

Role Title: Azure AI Engineer / Senior Azure AI/ML Engineer

Experience Level: 5+ Year

Role Overview

We are looking for an experienced Azure AI Engineer with 5+ years of hands‑on experience in cloud, artificial intelligence, and machine learning architectures to design, build, and operationalize enterprise‑grade AI and Generative AI solutions. In this role, you will leverage Azure AI Foundry, Azure OpenAI Service, and Azure Machine Learning (Azure ML) to build scalable RAG pipelines, deploy intelligent agents, and establish production MLOps/LLMOps workflows across enterprise workloads.

Key Responsibilities

1. Generative AI & LLM Solution Delivery

• Architect and implement end‑to‑end GenAI applications utilizing Azure OpenAI Service (GPT‑4o, reasoning models, and embedding models).
• Build advanced Retrieval‑Augmented Generation (RAG) architectures integrating Azure AI Search (vector, hybrid, and semantic re‑ranking).
• Develop, test, and orchestrate complex LLM workflows and multi‑agent systems using frameworks like Semantic Kernel, LangChain, LlamaIndex, or Prompt Flow.
• Fine‑tune, evaluate, and benchmark foundation models on domain‑specific datasets to meet accuracy, latency, and cost thresholds.

2. Model Lifecycle, Platform & Azure AI Foundry

• Leverage Azure AI Foundry (formerly Azure AI Studio) to unify model exploration, prompt design, evaluation metrics, and model catalog deployments.
• Build and manage machine learning training, tuning, and batch/real‑time inference pipelines in Azure Machine Learning (Azure ML).
• Containerize and deploy models as scalable endpoints using Azure ML Managed Online Endpoints, Azure Container Apps, or Azure Kubernetes Service (AKS).
• Manage model versioning, artifacts, metrics, and experimentation tracking using MLflow integrated with Azure ML.

3. LLMOps, Automation & Productionization

• Build and maintain CI/CD pipelines via Azure DevOps or GitHub Actions for automated testing, evaluation gating, and blue‑green deployment of AI models and RAG endpoints.
• Embed automated LLM evaluation suites to systematically measure hallucination, groundedness, relevance, and toxicity prior to deployment.
• Monitor runtime performance, inference latency, token usage, and compute costs using Azure Monitor, Application Insights, and custom telemetry dashboards.

4. Enterprise Security, Governance & Responsible AI

• Enforce Responsible AI guidelines using Azure AI Content Safety, runtime guardrails, prompt injection defenses, and PII masking.
• Secure AI infrastructure through Azure Virtual Networks (VNets), Private Endpoints, Azure Key Vault, Managed Identities, and strict Azure RBAC.

Required Technical Skills & Qualifications

Core Competencies:

• Experience: 5+ years of overall technical experience, with at least 2–3 years focused on building, deploying, and operating AI/ML and GenAI systems on Microsoft Azure.
• Azure AI & ML Stack:
• Azure AI Foundry (project hubs, prompt evaluation, model catalog).
• Azure OpenAI Service (API integration, quota management, fine‑tuning).
• Azure Machine Learning (Compute Clusters, Environments, Pipelines, Endpoints, Datasets).
• Azure AI Search (Vector search, indexing, semantic ranker).
• Programming & Frameworks:
• Advanced proficiency in Python (Pandas, NumPy, AsyncIO).
• Hands‑on experience with LLM orchestration frameworks: Semantic Kernel, LangChain, or LlamaIndex.
• Exposure to classical ML/DL libraries: scikit‑learn, PyTorch, or Hugging Face.
• Data & Storage: Strong SQL skills, along with experience handling Azure Blob Storage, Azure Data Lake Storage (ADLS Gen2), and Cosmos DB.
• DevOps & Infrastructure:
• Docker containerization.
• CI/CD automation via Azure DevOps or GitHub Actions.
• Basic Infrastructure as Code (IaC) using Terraform or Bicep.

Preferred Qualifications

• Certifications:
• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Microsoft Certified: Azure Data Scientist Associate (DP-100)