AI Data Architect

• 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 modelling, and marketing mix modelling. \n

• Advanced data analytics: Strong in statistical analysis, predictive modelling, and data storytelling. Turns complex datasets into actionable insights for business and executive stakeholders. \n

• BI and visualization: Proficient in Tableau, Power BI, Looker, and Google Analytics/GA4. Builds self-service dashboards and reporting layers. \n

• Data platforms: Hands-on with Snowflake, Databricks, BigQuery, Redshift, dbt, and ETL/ELT pipelines. Brings strong data modelling and data quality practices. \n

• 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. \n

• 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. \n

• 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. \n

• Cloud AI platforms: Expert in at least one of AWS (SageMaker, Bedrock), Azure (Azure ML, Azure OpenAI, AI Foundry), or GCP (Vertex AI). Also proficient in Kubernetes, Docker, and Terraform. \n

• MLOps and LLMOps: Experience with CI/CD for models, MLflow, Kubeflow, feature stores, and model registries. \n

• Languages and frameworks: Proficient in Python, R, SQL, PyTorch, TensorFlow, Hugging Face, and FastAPI. \n

• Responsible AI: Applies data privacy, security, and compliance requirements (GDPR, HIPAA, SOC 2) and AI governance practices to the design. \n

• 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. \n

• 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. \n

10+ years of experience in Data Analytics, Software Engineering, Data Engineering, or Machine Learning Engineering

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5+ years of hands-on experience in Advanced Analytics and Measurement

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3+ years of experience designing and delivering production-grade AI/ML and Generative AI solutions