Data Architect With AI

Strategic & Architectural Leadership

• Define and evolve AI & Data architecture 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.
• 12+ 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