: Candidates having excellent communication skills are preferred
Primary Responsibilities:
Design, develop, and deploy AI/ML models using Python, Azure Machine Learning, and GenAI models.
Integrate AI solutions with Microsoft Cloud services such as Azure Functions, Azure Data Lake, Azure Synapse, and Azure Cognitive Services.
Collaborate with data scientists, software engineers, and business stakeholders to understand requirements and deliver scalable solutions.
Optimize and monitor model performance in production environments.
Ensure security, compliance, and best practices in cloud-based AI deployments.
Stay current with emerging AI technologies and trends, especially within the Microsoft ecosystem
:
Bachelor's or master's degree in computer science, Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, Statistics, or a related field
5+ years of hands-on experience in machine learning engineering or AI/ML development using Python and cloud-based tools
Deep expertise in large language models (LLMs) such as GPT, Gemini, and related architectures
1+ years of experience working with Generative AI and Retrieval-Augmented Generation (RAG) systems in production environments
Proficient in designing and deploying LLM-powered applications using LangChain, LangGraph, and vector databases like FAISS and Pinecone
Strong understanding of deep learning architectures (e.g., Transformers) with practical experience using ML frameworks such as PyTorch, TensorFlow, Keras, and Scikit-learn
Proficiency in data manipulation and analysis using Pandas, NumPy, and other Python data libraries
Hands-on experience with Microsoft Azure, including Azure Machine Learning, Azure DevOps, and other cloud-native services for scalable ML workflows
Familiarity with REST APIs, containerization (Docker), and CI/CD pipelines
Excellent analytical, problem-solving, and communication skills, with the ability to collaborate effectively across cross-functional teams
Azure certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate).
Experience with MLOps and model lifecycle management.
Familiarity with healthcare data standards (X12, HL7) is a plus.
Experience with Power BI or other Microsoft analytics tools.