AI Data Architect

Location: All US time zones (Preferred – Columbus, Ohio and / OR Edison, NJ)- Remote also Option

Minimum required experience:

At least 10 years in data analytics, software, data, or ML engineering. This must include 5 or more years in advanced data analytics and measurement, and 3 or more years designing and delivering enterprise AI/ML and GenAI solutions in production. The candidate should have the following skills:

• Measurement and analytics: Designs measurement frameworks, KPIs, and success metrics that tie AI and business initiatives to measurable outcomes. Experienced with A/B testing, multivariate testing, causal inference, attribution modeling, and marketing mix modeling.
• Advanced data analytics: Strong in statistical analysis, predictive modeling, and data storytelling. Turns complex datasets into actionable insights for business and executive stakeholders.
• BI and visualization: Proficient in Tableau, Power BI, Looker, and Google Analytics/GA4. Builds self-service dashboards and reporting layers.
• Data platforms: Hands-on with Snowflake, Databricks, BigQuery, Redshift, dbt, and ETL/ELT pipelines. Brings strong data modeling and data quality practices.
• Architecture: Designs end-to-end AI and analytics architecture, including data ingestion, model training, serving, monitoring, measurement, and governance. Can design solutions that scale, stay secure, and keep costs under control.
• GenAI and agents: Hands-on with LLMs, RAG, vector databases (Pinecone, Weaviate, pgvector), prompt engineering, fine-tuning, agentic frameworks (LangChain, LlamaIndex, Semantic Kernel), and LLM evaluation and guardrails.
• AI performance measurement: Defines and tracks model and LLM quality metrics, ROI, adoption, and business impact. Builds observability and drift detection into production AI systems.
• Cloud AI platforms: Expert in at least one of AWS (SageMaker, Bedrock), Azure (Azure ML, Azure OpenAI, AI Foundry), or Google Cloud Platform (Vertex AI). Also proficient in Kubernetes, Docker, and Terraform.
• MLOps and LLMOps: Experience with CI/CD for models, MLflow, Kubeflow, feature stores, and model registries.
• Languages and frameworks: Proficient in Python, R, SQL, PyTorch, TensorFlow, Hugging Face, and FastAPI.
• Responsible AI: Applies data privacy, security, and compliance requirements (GDPR, HIPAA, SOC 2) and AI governance practices to the design.
• Leadership: Can lead client discovery, write solution proposals and estimates, guide analytics, ML, and data engineering teams, and explain insights and architecture decisions to executive stakeholders.
• Preferred: Cloud or AI certifications, such as AWS ML Specialty, Azure AI Engineer, or Google Professional ML Engineer, and analytics certifications, such as Tableau, Power BI, or GA4.