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Job Summary
The GenAI Data Automation Engineer will design and implement AI-driven automation and data engineering solutions across AWS and Azure hybrid environments. This role focuses on building intelligent, scalable data pipelines that integrate cloud services, enterprise tools, and Generative AI to support analytics, reporting, and customer engagement platforms. The ideal candidate is analytical, innovative, and mission-driven, with the ability to translate complex technical requirements into effective automated systems. This position requires U.S. citizenship and the ability to obtain a Public Trust clearance. The role is primarily remote, with occasional onsite work in the Gaithersburg area approximately once per quarter. Job Responsibilities
Design, develop, and maintain data pipelines using AWS services such as S3, RDS/SQL Server, Glue, Lambda, EMR, DynamoDB, and Step Functions.
Build and optimize ETL/ELT processes to transfer data between multiple systems, including DynamoDB to SQL Server (AWS) and AWS to Azure SQL.
Integrate AWS Connect and Nice inContact CRM data into enterprise pipelines for analytics and operational reporting.
Enhance data ingestion pipelines using Apache Spark, Flume, and Kafka for both real-time and batch processing into Apache Solr and AWS OpenSearch.
Leverage Generative AI frameworks (AWS Bedrock, Amazon Q, Azure OpenAI, Hugging Face, LangChain) to automate vector generation, embedding, data quality checks, metadata tagging, and lineage tracking.
Implement LLM-assisted data transformation, anomaly detection, and conversational BI interfaces for natural language access to data sources.
Develop AI-powered copilots for pipeline monitoring, diagnostics, and automated troubleshooting.
Optimize SQL Server performance through stored procedures, indexing, and query tuning.
Apply CI/CD best practices using GitHub, Jenkins, or Azure DevOps for data pipelines and GenAI model deployment.
Ensure security and compliance through IAM, encryption, VPC isolation, RBAC, and firewall configurations.
Participate in Agile DevOps processes, delivering sprint-based enhancements and AI-enabled features.
Job Requirements
Bachelor's degree in Computer Science or related field.
Minimum of 2 years of experience in data engineering and automation.
Hands-on experience with Generative AI frameworks such as AWS Bedrock, Azure OpenAI, or open-source platforms.
Strong proficiency in SQL, SSIS, Python, Spark, Bash, PowerShell, and cloud CLI tools.
Experience with AWS services including S3, Glue, RDS/SQL Server, Lambda, EMR, and DynamoDB.
Familiarity with Apache Flume, Kafka, and Solr for large-scale data ingestion and search.
Experience integrating REST APIs into data workflows.
Proficiency with CI/CD tools such as GitHub, Azure DevOps, Jenkins, and JIRA.
Strong troubleshooting and performance optimization skills for SQL and Spark environments.
Experience operationalizing Generative AI pipelines, including model deployment, monitoring, and lifecycle management.
Excellent communication and presentation skills.
U.S. citizenship and eligibility to obtain a Public Trust clearance.
Preferred Qualifications
Certifications such as AWS Data Engineer, AWS AI/ML Specialty, Azure AI Engineer, or Databricks Certified Data Engineer.
Experience implementing RAG pipelines, embeddings, and vector search with Solr, OpenSearch, FAISS, Pinecone, or Pgvector.
Experience with multi-cloud data integration between AWS and Azure SQL environments.
Familiarity with Microsoft BizTalk, SSIS, and data governance tools (Purview, Unity Catalog, AWS Glue Catalog).
Experience using Infrastructure-as-Code tools such as Terraform, CloudFormation, or Bicep.
Understanding of compliance frameworks such as FedRAMP, PCI-DSS, and HIPAA.
Additional Job Details
This position is primarily remote with occasional onsite requirements in the Gaithersburg area (approximately once per quarter).
Must be able to obtain and maintain a Public Trust clearance.
U.S. citizenship is required for consideration.
#DICE #CJ
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Equiliem
Nov 04, 2025