Solution Architect

Grant Thornton

10 - 14 years

30 - 45 Lacs

bengaluru

Posted:9 hours ago| Platform:

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Skills Required

openai azure machine learning microsoft ai azure cognitive services python microsoft azure

Work Mode

Hybrid

Job Type

Full Time

Job Description

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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