Solution Architect

1 day ago

Bengaluru, Karnataka, India Grant Thornton Full time ₹ 2,00,00,000 - ₹ 2,50,00,000 per year

Roles and Responsibilities

  • Design, develop, and deploy AI/ML models using Microsoft Azure AI services (e.g., Azure Machine Learning, Cognitive Services, OpenAI on Azure).
  • 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).

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