• Own end-to-end data and AI solution architecture across modern cloud data platforms • Design and implement enterprise data architectures across SOO → SOR → CDM (Curated) layers • Lead core Data Architecture disciplines, including: - conceptual, logical, and physical data modeling; MDM and reference data; metadata, lineage, and governance • Design and build scalable batch and real-time data pipelines using lakehouse and warehouse patterns • Enable and support AI / ML use cases on data platforms, including feature engineering, model integration, orchestration, and monitoring • Provide architectural leadership on production-grade systems, covering CI/CD, security, performance, and observability • Partner with engineers, delivery teams, and client stakeholders to align on architectural tradeoffs and delivery decisions • Act as a proactive thought partner, identifying opportunities for simplification, acceleration, and AI-driven value creation

• Design, develop, and maintain scalable data pipelines for batch and near-real-time use cases • Build and optimize Snowflake architecture, including schema design, data modeling, performance tuning, and warehouse optimization • Integrate data from APIs, databases, SaaS platforms, and enterprise systems into a unified data warehouse • Implement and manage ETL/ELT workflows using tools such as dbt, Airflow, or equivalent orchestration frameworks • Ensure data quality, reliability, governance, and proper documentation across the data lifecycle • Develop executive-level dashboards and reports using Tableau and/or Power BI • Translate business requirements into scalable data models and analytics-ready datasets • Analyze complex datasets to identify trends, performance drivers, and operational insights • Optimize dashboard performance, usability, and governance standards • Train business users and promote adoption of BI solutions across the organization • Partner with analysts, AI engineers, delivery managers, and business stakeholders to align data architecture with strategic objectives • Support AI and advanced analytics initiatives by providing clean, curated, and well-structured datasets • Contribute to best practices in data engineering, analytics standards, and platform governance

• As an AI Software & Data Engineer, you will design and build full-stack software systems, data platforms, and AI-powered MVPs for TechTorch clients. • Work end-to-end — from React/Next.js frontends, through backend development in Python and data pipelines, to AI model integration and deployment — owning solutions from concept through production. • Design and build full-stack applications, including frontend interfaces using React and Next.js, backend services and APIs using Python and cloud-native patterns. • Develop secure, scalable application logic that integrates with data platforms and enterprise systems. Implement CI/CD pipelines, testing, and production-readiness standards. • Collaborate closely with product, data, and AI teams to deliver cohesive user experiences. • Design, build, and maintain scalable data pipelines and ELT workflows. Model and manage data in modern data platforms (e.g., Snowflake, Redshift, BigQuery, lakehouse architectures). • Ensure data quality, performance, and lineage across structured and semi-structured data. Enable analytics, reporting, and downstream AI use cases through well-designed data foundations. • Build AI prototypes and MVPs end-to-end for clients — from data ingestion to user-facing experience. Integrate AI/ML capabilities into software and data workflows (e.g., NLP, document intelligence, classification, anomaly detection). • Leverage modern AI platforms and services such as AWS Bedrock, SageMaker, Snowflake Cortex, OpenAI. Support feature engineering, inference pipelines, and AI-enabled automation. • Ensure AI solutions are observable, explainable, and suitable for enterprise and regulated environments.

• Design, build, and maintain scalable data pipelines using Snowflake as the central data platform • Develop AI-ready data models and feature layers to support analytics, ML, and GenAI use cases • Leverage Snowflake Cortex AI for embedding, classification, summarization, and AI-assisted analytics • Integrate and operationalize AI workflows using AWS Bedrock and related AWS services (e.g., Lambda, Step Functions) • Build and optimize ELT pipelines using tools such as dbt, SQL, and Python • Integrate data from diverse sources including APIs, SaaS platforms, databases, and event streams • Ensure data quality, observability, and governance across pipelines and AI workloads • Collaborate with AI engineers, data scientists, and business teams to translate use cases into scalable data solutions • Document data models, pipelines, and AI-related design decisions clearly for long-term maintainability