AI Data Architect

We are looking for a highly experienced Senior AI & Data Analytics Architect to join our team and lead the design, development, and deployment of enterprise-scale AI, ML, and GenAI solutions.

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

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

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

• 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.
• Advanced data analytics: Strong in statistical analysis, predictive modelling, 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, Big Query, Redshift, dbt, and ETL/ELT pipelines. Brings strong data modelling 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 (Lang Chain, Llama Index, 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 (Sage Maker, Bedrock), Azure (Azure ML, Azure OpenAI, AI Foundry), or GCP (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, Tensor Flow, 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.