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Strictly who are local to Atlanta GA only (No third party)
Job Description
Define and evolve AI & Data strategy and roadmap, aligned with business priorities and IT strategy.
Serve as a thought leader for modern data, analytics, and AI architectures, including Generative AI and Agentic AI.
Identify, evaluate, and recommend emerging technologies, platforms, and architectural patterns.
Partner with business and digital leaders to identify and prioritize high-impact AI and analytics use cases.
Provide architectural guidance on ethical, responsible, and compliant AI adoption.
Solution Architecture & Platform Design
Lead end-to-end architecture design for complex data, analytics, and AI initiatives, ensuring scalability, performance, security, and cost efficiency.
Design and govern cloud-based data platforms leveraging:
Google Cloud Platform (BigQuery, Vertex AI, Dataflow, Dataproc, Looker)
AWS (S3, Glue, EMR, Redshift, SageMaker, Lambda)
Snowflake (data warehouse, data sharing, performance optimization)
Architect modern enterprise data architectures, including:
Data Lake, Lakehouse, Data Mesh, and Data Fabric
Open table/file formats such as Parquet, Iceberg, Delta Lake
Medallion architectures (Bronze/Silver/Gold)
Define data ingestion and integration patterns across structured and semi-structured sources (SAP, Oracle, Salesforce, JDE, Ariba, IoT, APIs, NoSQL).
Define and enforce data quality, metadata, lineage, and access control standards.
AI, ML, and Generative AI Architecture
Design and implement AI/ML and GenAI solution architectures from experimentation through production.
Architect solutions for core ML use cases such as demand forecasting, predictive maintenance, supply chain optimization, and customer analytics.
Lead architecture for Generative AI and Agentic AI, including:
LLM integration with tools, APIs, and knowledge bases (RAG patterns)
Autonomous and semi-autonomous agent workflows
Fine-tuning, prompt engineering, and optimization strategies
Establish MLOps and LLMOps frameworks for model training, deployment, monitoring, evaluation, and lifecycle management.
Define approaches for model observability, explainability (XAI), bias detection, and risk mitigation.
Technical Leadership & Collaboration
Provide technical leadership and mentorship to solution architects, data engineers, data scientists, and AI engineers.
Collaborate closely with platform, DevOps, and cloud engineering teams to enable automation-driven deployments.
Review solution designs, conduct architecture assessments, and provide impact analysis and recommendations.
Communicate complex technical concepts clearly to both technical and executive audiences.
Required Qualifications
Bachelor’s Degree in Engineering or a related technical discipline.
14 years of hands-on experience in data architecture, analytics solutions, and/or cloud data platforms.
3 years of hands-on experience delivering AI/ML and Generative AI solutions in production.
6 years of experience designing and scaling enterprise data platforms on Google Cloud Platform, AWS, and Snowflake.
Preferred Qualifications
Master’s degree or Ph.D. preferred.
Demonstrated success leading large-scale, cross-functional data and AI initiatives.
Cloud platforms: Google Cloud Platform and AWS (multi-cloud experience strongly preferred)
Data platforms: Snowflake, BigQuery, Data Lakes, Lakehouse architectures
Programming & analytics: Python, SQL, PySpark
AI/ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost
GenAI/LLM frameworks, vector databases, and graph databases
Data engineering tools: Spark, Kafka, Hadoop
Containerization and orchestration: Docker, Kubernetes
CI/CD and DevOps practices
Strong understanding of data modeling, performance tuning, and cost optimization
Strong architectural thinking and problem-solving skills
Excellent communication and stakeholder management capabilities
Ability to influence without authority and operate effectively in matrixed organizations
Self-driven, organized, and able to manage multiple priorities
Preferred Certifications
AWS Certified Solutions Architect
Google Cloud Professional Cloud Architect
Snowflake or Data Engineering certifications
Strictly who are local to Atlanta GA only (No third party)
Job Description
Define and evolve AI & Data strategy and roadmap, aligned with business priorities and IT strategy.
Serve as a thought leader for modern data, analytics, and AI architectures, including Generative AI and Agentic AI.
Identify, evaluate, and recommend emerging technologies, platforms, and architectural patterns.
Partner with business and digital leaders to identify and prioritize high-impact AI and analytics use cases.
Provide architectural guidance on ethical, responsible, and compliant AI adoption.
Solution Architecture & Platform Design
Lead end-to-end architecture design for complex data, analytics, and AI initiatives, ensuring scalability, performance, security, and cost efficiency.
Design and govern cloud-based data platforms leveraging:
Google Cloud Platform (BigQuery, Vertex AI, Dataflow, Dataproc, Looker)
AWS (S3, Glue, EMR, Redshift, SageMaker, Lambda)
Snowflake (data warehouse, data sharing, performance optimization)
Architect modern enterprise data architectures, including:
Data Lake, Lakehouse, Data Mesh, and Data Fabric
Open table/file formats such as Parquet, Iceberg, Delta Lake
Medallion architectures (Bronze/Silver/Gold)
Define data ingestion and integration patterns across structured and semi-structured sources (SAP, Oracle, Salesforce, JDE, Ariba, IoT, APIs, NoSQL).
Define and enforce data quality, metadata, lineage, and access control standards.
AI, ML, and Generative AI Architecture
Design and implement AI/ML and GenAI solution architectures from experimentation through production.
Architect solutions for core ML use cases such as demand forecasting, predictive maintenance, supply chain optimization, and customer analytics.
Lead architecture for Generative AI and Agentic AI, including:
LLM integration with tools, APIs, and knowledge bases (RAG patterns)
Autonomous and semi-autonomous agent workflows
Fine-tuning, prompt engineering, and optimization strategies
Establish MLOps and LLMOps frameworks for model training, deployment, monitoring, evaluation, and lifecycle management.
Define approaches for model observability, explainability (XAI), bias detection, and risk mitigation.
Technical Leadership & Collaboration
Provide technical leadership and mentorship to solution architects, data engineers, data scientists, and AI engineers.
Collaborate closely with platform, DevOps, and cloud engineering teams to enable automation-driven deployments.
Review solution designs, conduct architecture assessments, and provide impact analysis and recommendations.
Communicate complex technical concepts clearly to both technical and executive audiences.
Required Qualifications
Bachelor’s Degree in Engineering or a related technical discipline.
14 years of hands-on experience in data architecture, analytics solutions, and/or cloud data platforms.
3 years of hands-on experience delivering AI/ML and Generative AI solutions in production.
6 years of experience designing and scaling enterprise data platforms on Google Cloud Platform, AWS, and Snowflake.
Preferred Qualifications
Master’s degree or Ph.D. preferred.
Demonstrated success leading large-scale, cross-functional data and AI initiatives.
Cloud platforms: Google Cloud Platform and AWS (multi-cloud experience strongly preferred)
Data platforms: Snowflake, BigQuery, Data Lakes, Lakehouse architectures
Programming & analytics: Python, SQL, PySpark
AI/ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost
GenAI/LLM frameworks, vector databases, and graph databases
Data engineering tools: Spark, Kafka, Hadoop
Containerization and orchestration: Docker, Kubernetes
CI/CD and DevOps practices
Strong understanding of data modeling, performance tuning, and cost optimization
Strong architectural thinking and problem-solving skills
Excellent communication and stakeholder management capabilities
Ability to influence without authority and operate effectively in matrixed organizations
Self-driven, organized, and able to manage multiple priorities
Preferred Certifications
AWS Certified Solutions Architect
Google Cloud Professional Cloud Architect
Snowflake or Data Engineering certifications