AI/ML Architect

AI//ML Architect

Location: NewYork City, NY ( 3 days Onsite 2 days Remote) 

Experience: 12+ Years

Job Overview:

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