Azure Data Engineer - Lead Consultant

Key Responsibilities:

• Design, develop, and deploy AI/ML models using Microsoft Azure AI services (e.g., Azure Machine Learning, Cognitive Services, OpenAI on Azure) and have experience on Agentic Frameworks.
• Collaborate with stakeholders to translate business challenges into AI-based solutions.
• Lead end-to-end AI projects: from data collection and preprocessing to model training, evaluation, and deployment.
• Integrate open-source frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) into Microsoft cloud environments.
• Optimize AI pipelines using MLOps tools (e.g., Azure ML Pipelines, GitHub Actions, MLflow).
• Develop data science models for predictive analytics, NLP, computer vision, or generative AI applications.
• Champion responsible AI practices, data governance, model fairness, and transparency.
• Mentor junior data scientists and engineers; conduct code reviews and knowledge-sharing sessions.

Required Qualifications:

• 10+ years of experience in AI, machine learning, or data science roles.
• Strong experience with Microsoft AI/ML stack: Azure Machine Learning, Azure Cognitive Services, Power BI with AI, Azure Synapse Analytics.
• Proficiency in open-source AI frameworks: TensorFlow, PyTorch, Hugging Face, Scikit-learn.
• Strong programming skills in Python (preferred), R, or C#.
• Deep understanding of data modeling, statistical analysis, and algorithm development.
• Experience with cloud-native AI deployments and containerization (Docker, Kubernetes).
• Hands-on knowledge of MLOps, CI/CD pipelines, and AI lifecycle management.
• Experience working in agile and cross-functional teams.
• Excellent communication, leadership, and presentation skills.

Preferred Qualifications:

• Microsoft certifications such as:
• Azure AI Engineer Associate
• Azure Data Scientist Associate
• Azure Solutions Architect Expert
• Experience integrating AI into Microsoft Power Platform and Dynamics 365.
• Exposure to generative AI (e.g., OpenAI, GPT models on Azure, LLM fine-tuning).
• Experience in multi-cloud or hybrid-cloud AI architecture.
• Familiarity with open-source data versioning and pipeline tools (e.g., DVC, Kedro, Prefect).