Ai Python Technical Architect_2232

Qualifications & Experience

• Bachelor’s degree in Computer Science, Engineering, or a related field.


• 10–12 years of overall IT experience, with 3–5 years in an Architect or Technical Architect role.


• Proven experience designing and implementing enterprise-scale solutions across multiple domains such as data platforms, cloud, integration, and application modernization.


• Experience with enterprise architecture frameworks such as TOGAF or Zachman (preferred).


• Hands-on experience with at least one major cloud platform (Azure, AWS, or GCP), including cloud-native services.

Role & Responsibilities

• Define and oversee the end-to-end architecture of the Solutions, ensuring scalability, security, interoperability, and future readiness.


• Translate business objectives into technical blueprints, reusable solution frameworks, AI reference architectures, and integration standards.


• Design and implement architecture patterns for multi-system integration, API orchestration, data harmonization, and AI-driven workflows.


• Architect GenAI-enabled capabilities such as intelligent assistants, copilots, retrieval-augmented generation (RAG), and automation pipelines.


• Govern solution lifecycle management, versioning, and alignment with enterprise architecture principles, including AI governance and responsible AI standards.


• Collaborate closely with product owners, AI engineers/data scientists, developers, and operations teams to ensure cohesive and scalable solution delivery.


• Evaluate emerging technologies-including GenAI platforms, agentic frameworks, vector databases, and orchestration tools and recommend architectural improvements for performance, scalability, and cost optimization.


• Establish and maintain architecture documentation, AI design patterns, system diagrams, and best-practice guidelines.


• Provide technical leadership during architecture reviews, PoCs, AI pilots, and enterprise-scale implementations, ensuring measurable business value.


• Ensure compliance with security, data privacy, regulatory, and enterprise governance standards, including model governance, data lineage, and auditability.