Data & AI Architect

Siri InfoSolutions Inc

Allentown, PA, United States

4 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Compensation

$150,000.0 - $160,000.0

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Artificial Intelligence Microsoft Azure Continuous Integration Data as a Services Data Architecture Data Centers Python (Programming Language) Machine Learning Tensorflow Azure Machine Learning Azure Data Lake SQL Databases

+17 more

Feature Engineering Azure Data Factory Pytorch Apache Spark Modularization Containerization Data Lakes Scikit Learn Kubernetes Machine Learning Operations Api Design Azure Synapse Analytics Software Version Control Data Pipelines Serverless Computing Docker Databricks

Job description

Architect end-to-end AI/ML solutions using Azure services (Azure ML, Synapse, Data Lake, etc.) and Databricks.
Lead technical design sessions and guide teams on best practices for scalable and secure AI solutions.
Collaborate with data scientists, engineers, and business stakeholders to translate business problems into AI solutions.
Design and implement MLOps pipelines for model training, deployment, monitoring, and governance.
Optimize data pipelines and feature engineering workflows using Spark and Delta Lake on Databricks.
Ensure compliance with data privacy, security, and governance standards.
Evaluate and integrate emerging AI technologies and framework
Provide technical leadership and mentorship to junior architects and engineers.

Requirements

Strong expertise in Microsoft Azure AI & Data services: Azure Machine Learning, Azure Synapse, Azure Data Factory, Azure Data Lake, Azure Functions.
Hands-on experience with Databricks: Spark, Delta Lake, MLflow, notebooks, and job orchestration.
Proficiency in Python, SQL, and ML frameworks like TensorFlow, PyTorch, Scikit-learn.
Experience with MLOps tools and practices: CI/CD, model versioning, monitoring, and retraining., * Deep understanding of data architecture, feature stores, and real-time inference.
Familiarity with containerization (Docker, Kubernetes) and API development for model serving.
Excellent communication and stakeholder management skill
Azure certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert) are a plus.