Key Responsibilities

AI/ML Solution Design & Development

• Design, build, and optimize supervised, unsupervised, and deep learning models.
• Select appropriate algorithms, frameworks, and architectures to meet business needs.
• Perform data preprocessing, feature engineering, and exploratory data analysis.

Cloud Architecture & Integration

• Architect scalable AI/ML solutions leveraging:
• Azure: Machine Learning, Cognitive Services, Databricks, Synapse, Data Lake, Event Hubs.
• AWS: SageMaker, Rekognition, Comprehend, Redshift, S3, Kinesis.
• Integrate ML pipelines with data lakes, data warehouses, APIs, and microservices.
• Ensure adherence to cloud best practices, security, and compliance frameworks.

MLOps & Automation

• Implement CI/CD pipelines for model training, validation, and deployment.
• Use Azure DevOps, GitHub Actions, AWS CodePipeline, and IaC tools (Terraform, CloudFormation) for automation.
• Monitor model performance, drift, and retraining processes.

Collaboration & Stakeholder Engagement

• Partner with data engineers, data scientists, DevOps engineers, and product teams to deliver business-aligned AI/ML solutions.
• Present solution designs, performance metrics, and recommendations to technical and non-technical stakeholders.

Security, Compliance & Governance

• Apply data privacy and compliance measures (HIPAA, GDPR, SOC 2).
• Incorporate Responsible AI principles (fairness, transparency, explainability).

Required Skills & Qualifications:

• Bachelor’s or Master’s in Computer Science, Data Science, AI/ML, or related field.
• 5+ years of experience in AI/ML development and deployment.
• 3+ years of hands-on experience with Azure and AWS AI/ML services.
• Proficiency in Python, R, and ML libraries: TensorFlow, PyTorch, scikit-learn, Hugging Face.
• Strong knowledge of cloud data storage, processing, and streaming platforms.
• Experience with Docker, Kubernetes (AKS/EKS) for containerized ML workloads.
• Familiarity with big data frameworks (Spark, Databricks).