Key Responsibilities: Design, build, and deploy scalable machine learning models and algorithms using Azure ML and other cloud-based services. Architect end-to-end ML pipelines, including data preprocessing, feature engineering, model training, validation, deployment, and monitoring. Use Azure Machine Learning Studio, Azure Databricks, and other platform tools to accelerate the development and deployment of ML models. Collaborate with cross-functional teams (data scientists, developers, and business stakeholders) to understand business problems and translate them into technical solutions. Perform data analysis and modeling to provide insights and actionable recommendations to clients. Optimize ML models for performance, scalability, and cost-effectiveness within a cloud environment. Provide consulting expertise to clients by presenting technical recommendations and solution roadmaps tailored to their business goals. Support cloud migrations, integrations, and system scaling related to AI/ML. Stay updated on trends in AI, ML, and cloud technologies to recommend best practices and innovative solutions. Document ML workflows and frameworks, ensuring transparency and reusability across projects.
Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. Proven detail oriented experience as a Machine Learning Engineer with a focus on developing, deploying, and managing ML models in Azure environments. Hands-on expertise in Azure tools and services, such as Azure Machine Learning Studio, Azure Databricks, Azure Data Factory, and Azure DevOps. Proficiency in Python, R, or similar programming languages commonly used in ML. Knowledge of ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Experience with MLOps strategies, automating ML pipelines, and model lifecycle management. Familiarity with SQL, NoSQL databases, and big data processing tools (e.g., Spark). A proven track record of consulting experience, including working directly with clients to identify needs, communicate solutions, and deliver business value. Strong analytical, problem-solving, and decision-making skills. Excellent communication and presentation abilities to explain complex technical concepts to non-technical stakeholders. Experience with Agile development methodologies and project management in consulting environments.

Preferred Qualifications: Azure Certifications, such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate. Familiarity with responsible AI best practices and ethical ML development. Experience with cloud cost optimization for ML workloads. Knowledge of additional cloud providers (AWS, Google Cloud) is a plus.

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