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# AI ML Data Engineer

- **Company:** Cloudious LLC
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Big Data, Cloud Computing, Cloud Engineering, Continuous Integration, Information Engineering, Data Governance, Data Integration, Data Security, DevOps, Github, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Data Mesh, Software Engineering, SQL Databases, Data Streaming, Enterprise Software Applications, Delivery Pipeline, Snowflake, Apache Spark, Agentic-AI, Git, Core Data, Deployment Automation, Health Level Seven International, Data Management, Machine Learning Operations, Intersystems, Terraform, Artificial Intelligence Markup Language (AIML), Software Version Control, Data Pipelines, Jenkins, Databricks
- **Published:** October 30, 2026
- **Apply:** https://www.careerjet.com/jobad/usf6f2a25a4d4e236394d50241451f5658

## About the Role

l Experience Required: 10+ Years with Databricks, ADF,SQL
l We are seeking a highly skilled Senior Data Engineer with a strong focus on data engineering and experience in AI/ML solutions.
l The ideal candidate will have a proven track record in building and maintaining data pipelines, data models, and data integration workflows., l 5+ years of experience in software development with a strong focus on Data Engineering.
l 1+ years of experience delivering solutions that include AI ML or Generator components.
l Proven experience building and maintaining ML Pipelines, Data Modelling, and Data Integration workflows.
l Strong Basic SQL skills for querying, transformation, and performance optimization.
l Advanced proficiency in Python and experience with Scala for data and AI workloads.
l Hands-on experience with Spark (batch and streaming), Databricks, SparkSQL, and Cloud Platforms.
l Experience with Azure IaaS, cloud infrastructure, monitoring, and deployment automation.
l Experience with Continuous Integration and Continuous Delivery (CI/CD), version control, and CI CD & DevOps practices (Git, GitHub Actions, Jenkins OpenSource)
PREFERRED SKILLS:
l Experience with Databricks ML, Databricks, and Agentic AI solutions.
l Knowledge of MDM, Data Governance, and data governance frameworks.
l Experience in healthcare, insurance, or regulated industries.
l Familiarity with Intersystems HealthShare, HL7 reporting standards, and healthcare data standards.
l Experience with Terraform, Infra as code, and Robotic Process Automation.
l Azure Databricks certifications.
l Expertise You'll Bring: Strong stakeholder management and cross-functional collaboration skills.
l Knowledge of data security, privacy, and Enterprise Architecture best practices.
l Proven success delivering master data, analytics, or AI-enabled data products using Azure Databricks, Snowflake, and cloud platforms.
l Experience with Data Governance, MDM, Data Mesh, and regulated environments is highly valued.

## Description

l Design, build, and maintain scalable, secure, and high-performance batch and streaming data pipelines using modern data engineering practices.
l Lead the modernization and migration of on-premise data platforms to cloud-native architectures, overseeing infrastructure, networking, security, and deployment pipelines.
l Develop and maintain core data platform components that support reliable ingestion, transformation, enrichment, and consumption of enterprise data assets.
l Collaborate with data domain experts across various departments to understand data sources and design efficient, connected data models.
l Ensure data accuracy, quality, relevance, and governance by implementing validation, monitoring, documentation, and security controls.
l Analyze large datasets to identify trends, patterns, and actionable insights; perform data analysis to support analytics and AI use cases.
l Enable reporting and analytics by making curated, trusted datasets available to BI and downstream consumers.
l Partner with AI/ML Engineers to design, build, and improve machine learning models and AI-powered enterprise applications.
l Optimize data and AI pipelines for scalability, performance, reliability, and cost efficiency in production environments.
l Mentor and guide engineers and interns, contributing to skill development in data engineering and AI/ML.
l Collaborate effectively with cross-functional teams and stakeholders, communicating complex technical concepts clearly