Define end-to-end AI system architectures. Aligns business objectives with model design, data integration, and infrastructure, ensuring scalability, observability, and compliance across the full lifecycle.

Develop and validate statistical models and machine learning algorithms. Responsible for data exploration, feature engineering, model evaluation, and continuous improvement through experimentation.

Design, build, and maintain data pipelines and storage systems. Ensure reliable data ingestion, transformation, and availability for downstream analytics and AI workloads.

Implement and optimize machine learning and generative AI models for production environments. Handle model deployment, RAG pipelines, inference optimization, and performance monitoring.

Develop and integrate backend and frontend systems that operationalize AI services. Focus on scalable APIs, microservices, and secure application delivery aligned with cloud-native architectures.

Design and implement conversational interfaces for copilots and AI agents. Combine UX principles with NLP techniques to improve usability, clarity, and user trust.

Maintain and optimize large language model operations. Oversee prompt management, evaluation frameworks, and telemetry to ensure quality, accuracy, and responsible AI outputs.

Implement and enforce security controls across AI and data systems. Manage identity, access, encryption, and compliance with frameworks such as ISO 27001 and NIST AI RMF.

Deploy and manage distributed systems for AI workloads. Optimize infrastructure for performance, scalability, and cost using multi-cloud and container orchestration frameworks.