AI Engineer at Blend360

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

04 Aug, 26

Salary

0.0

Posted On

06 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Azure AI, Machine Learning, Generative AI, Microsoft Fabric, Azure AI Foundry, Azure OpenAI, RAG Architecture, LangChain, LangGraph, MLOps, LLMOps, Python, PyTorch, TensorFlow, Scikit-learn, Azure DevOps

Industry

Professional Services

Description

Company Description Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com We are seeking a AI Engineer to contribute to our next level of growth and expansion. Job Description We are seeking a skilled AI Engineer with strong hands-on experience in Azure AI and Machine Learning services. The ideal candidate will design, develop, and deploy scalable AI/ML and Generative AI solutions leveraging Azure Cloud technologies, Microsoft Fabric, and Azure AI Foundry to solve complex business challenges and drive innovation. Key Responsibilities Design, develop, and deploy AI/ML models using Azure Machine Learning, Azure Databricks, and Microsoft Fabric. Build and operationalize LLM-based solutions leveraging Azure AI Foundry, Azure OpenAI, and Cognitive Services. Implement RAG-based architectures using Azure AI Search, Vector Databases, and LangChain or LangGraph frameworks. Collaborate with Data Engineers and Architects to integrate AI applications with Microsoft Fabric datasets ensuring governance, data lineage, and reliability. Implement MLOps and LLMOps best practices for continuous integration, delivery, and monitoring of AI models using Azure ML Pipelines and Azure DevOps. Optimize model performance and ensure scalability, security, and reliability of deployed solutions. Work closely with business and technical stakeholders to translate requirements into AI-driven solutions. Stay up to date with emerging trends in AI, GenAI, and cloud-based ML technologies. Qualifications 5–8 years of experience in building and deploying AI/ML or GenAI solutions in production environments. Strong expertise in Azure Machine Learning, Azure Databricks, Microsoft Fabric, Azure AI Foundry, and Azure Cognitive Services. Proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn). Hands-on experience with RAG architecture, prompt engineering, and LLM-based application development. Experience with MLOps/LLMOps pipelines, model tracking, and CI/CD using Azure DevOps. Familiarity with data integration across Microsoft Fabric, Azure Data Factory, or Synapse. Strong understanding of model lifecycle management, monitoring, and performance optimization using MLflow, App Insights, and Azure Monitor. Excellent problem-solving, debugging, and collaboration skills. Good to Have Exposure to containerization and deployment tools (Docker, Kubernetes). Experience building APIs and model endpoints using FastAPI or Flask. Understanding of Data Engineering concepts, ETL workflows, and governance within Microsoft Fabric. Azure certifications such as Azure AI Engineer Associate or Azure Data Scientist Associate. Familiarity with Git, Terraform, or other Infrastructure-as-Code tools. Education Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

Responsibilities

Design, develop, and deploy scalable AI/ML and Generative AI solutions using Azure Cloud technologies and Microsoft Fabric. Implement RAG-based architectures and MLOps/LLMOps best practices to ensure model reliability and performance.