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AI/ML Architect

Kanshe InfotechNew York, NY🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationNew York, NY, United States
DockerSQLGitMLOpsOAuthSSOScikit-learnSnowflakeAirflowAngularGitHub ActionsGrafanaKafkaKerasPrometheusPyTorchPythonTensorFlowAzureKubernetesMachine LearningPower BICI/CDXGBoost

Job description

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

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