Job description

Job Description: an experienced AI / ML Architect to lead the design, development, and deployment of advanced artificial intelligence, including machine learning and large language models. The ideal candidate will have a strong background in data science, software engineering, and cloud technologies, with a proven track record of architecting scalable and robust AI/ML systems. You will collaborate with cross-functional teams to translate business requirements into technical solutions, ensuring best practices in model development, deployment, monitoring, security, and governance. Responsibilities: • Design end-to-end AI/ML architectures, including data pipelines, model training, deployment, and monitoring frameworks. • Ensure the perspectives of DevOps/MLOps, DevSecOps, FinOps, and Governance are addressed. • Leverage existing infrastructure wherever possible (Snowflake / Dataiku / Power BI / Azure). • Collaborate with data scientists, engineers, and business stakeholders to define project requirements and deliverables. • Evaluate and select appropriate AI/ML frameworks, tools, and platforms based on project needs. • Ensure scalability, reliability, and security of AI/ML solutions in production environments. • Oversee the integration of AI/ML models into existing products and services. • Establish and enforce best practices for model versioning, reproducibility, and governance. • Mentor and guide junior team members in AI/ML methodologies and architectural patterns. • Stay current with industry trends, emerging technologies, and research in AI/ML. Technologies: • ML/DL Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost • DevOps / MLOps: GitHub, GitHub Actions, CI/CD pipelines • DevSecOps: RBAC, SSO / SCIM, OAuth, Snowflake’s security model • Data Visualization: Power BI, Angular • Monitoring & Orchestration: Dagster, Airflow, Grafana, Prometheus • Data Ingestion: Airbyte, Snowpipe Files & Streaming, Kafka, APIs • Data Processing: Dataiku, Spark, Snowflake (streams / tasks / dynamic tables ), Python, SQL • Compute: Snowflake Warehouses & Compute Pools, Azure VM’s, Kubernetes, Docker • Storage: Snowflake, SQL, Iceberg Tables, Parquet, ADLS • Cloud Platforms: Azure

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