We are seeking an experienced AI/ML Architect to lead the design and delivery of scalable, enterprise-grade artificial intelligence and machine learning solutions. The ideal candidate combines strong technical expertise with architectural vision to transform business objectives into intelligent, data-driven systems, with a deep dive expertise in ML frameworks, data pipelines, model lifecycle management, and cloud-based AI services.

Key Responsibilities :

- Design End-to-End AI/ML Solutions using scalable architectures covering data ingestion, feature engineering, model development, training, deployment, and monitoring using Azure ML, Databricks, Synapse, ADF, and Snowflake (Snowpark, Streams, Tasks). Leverage AI Foundry for enterprise-grade AI workflows, model orchestration, and automation.

- Define and implement frameworks for model versioning, experiment tracking, retraining pipelines, CI/CD, and monitoring using MLflow, Azure ML Pipelines, AI Foundry, Docker, Kubernetes, and GitHub Actions.

- Evaluate and recommend optimal ML algorithms, frameworks, and cloud services (e.g., Azure ML, Snowflake, Databricks).

- Drive AI Strategy, collaborate with business and data stakeholders to align AI/ML initiatives and roadmap with organizational goals and measurable outcomes.

- Ensure Quality, Performance & Compliance are implemented using standards for data privacy, model fairness, explain-ability, and operational excellence.

- Mentor and Lead Teams on solution design, best practices, and reusable accelerators.

Preferred Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, or related field.

- 8+ years in data science, ML, or AI engineering, with at least 2 years in a solution/architecture role.

- Hands-on experience with Python, SQL, ML frameworks (TensorFlow, PyTorch, Scikit-learn), and MLOps tools (MLflow, Azure ML Pipelines, Docker/Kubernetes).

- Proven experience deploying and optimizing ML models in Azure cloud and integrating with Snowflake data platforms.

- Solid understanding of data pipelines, feature stores, cloud-native AI/ML architectures, and ETL workflows.