AI / ML Architect with strong snowflake Exp

Role: AI / ML Architect with strong snowflake Exp
Salisbury, MD (Remote)

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