Azure ML Engineer

Lykora Consulting Services Private Limited; Mumbai; Sur place; INR 1 500 000 - 2 800 000; Plein temps

Résumé du poste

Lykora Consulting Services Private Limited is seeking an AI/ML Engineer to design and implement AI solutions using Azure ML, AI Studio, and AI Services. The role involves building and deploying Python-based ML models via pipelines and SDKs, and integrating prebuilt AI capabilities into enterprise apps.

Candidates should have strong Python expertise, familiarity with MLops, and hands-on experience with Azure AI tools like ML Designer and AutoML, plus monitoring and optimization of AI workloads.

Qualifications

• Design and implement AI solutions using Azure Machine Learning, Azure AI Studio, and Azure AI Services.

Responsabilités

• Build and deploy Python-based machine learning models using Azure ML pipelines and SDKs.

Connaissances

Python

Azure ML

ML pipelines

MLOps

Azure AI Studio

Azure AI Services

Model monitoring

Performance optimization

LangChain

LlamaIndex

Outils

MLflow

Azure DevOps

GitHub Actions

LangChain

LlamaIndex

Google ADK

Description du poste

Location- Mumbai (Preferred only Mumbai candidates)

Mandatory Skills:

Job Description

• Design and implement AI solutions using Azure Machine Learning, Azure AI Studio, and Azure AI Services.
• Build and deploy Python-based machine learning models using Azure ML pipelines and SDKs.
• Integrate prebuilt AI capabilities (Vision, Language, Speech, Document Intelligence) into enterprise applications.
• Monitor AI workloads using Azure Monitor, Application Insights, and MLOps best practices.
• Continuously optimize model performance, cost-efficiency, and reliability.
• Experience with Azure AI Studio, Azure ML Designer, and AutoML.
• Knowledge of MLOps practices and tools such as MLflow, Azure DevOps, or GitHub Actions.
• Microsoft certifications (e.g., Azure AI Engineer Associate) are a plus.
• Programming: Strong proficiency in Python is mandatory, including OOP concepts and best coding practices.
• AI Frameworks: Familiarity with LLM frameworks and libraries such as Google ADK, LangChain, LlamaIndex, or similar frameworks.
• AI Techniques: Strong understanding of RAG architectures, prompt engineering, model optimization, and performance evaluation methodologies.