Key Responsibilities AI Solution Architecture
Design end-to-end AI, ML, GenAI, and Agentic AI architectures aligned to business outcomes.
Define solution blueprints covering data ingestion, model lifecycle, orchestration, deployment, monitoring, and governance.
Create architecture patterns and reusable frameworks that accelerate AI delivery.
Evaluate and select appropriate AI technologies, platforms, and tooling.
Technical Leadership
Provide architecture leadership across multiple initiatives and delivery teams.
Guide engineering teams on architectural best practices and AI adoption.
Support architectural reviews, design governance, and technology decisions.
Collaborate with Enterprise Architecture and platform teams to align with strategic roadmaps.
Generative AI & Agentic AI
Design architectures using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, orchestration frameworks, and agent-based systems.
Define patterns for prompt management, evaluation, observability, and AI safety controls.
Support the adoption of secure and governed GenAI capabilities across the organisation. [AI Enterpr...ategy v5.1 | PowerPoint], [Build your...n’t sure w | Viva Engage]
Governance, Risk & Security
Ensure AI solutions comply with Responsible AI, security, privacy, and risk requirements.
Contribute to model governance, architecture standards, and technical assurance processes.
Partner with Cyber Security, Risk, and Compliance teams to design secure AI capabilities.
Embed monitoring, auditability, explainability, and operational controls into AI solutions. [AI Enterpr...ategy v5.1 | PowerPoint]
Stakeholder Engagement
Translate complex technical concepts into business-friendly language.
Influence senior technical and business stakeholders.




Facilitate architecture workshops and solution design sessions.
Build strong relationships across product, engineering, architecture, and business teams.
Essential Skills & Experience
Strong experience designing enterprise-scale AI and Machine Learning solutions.
Experience with Generative AI, LLMs, Agentic AI frameworks, and RAG architectures.
Expertise in cloud-native architecture, preferably Google Cloud Platform (GCP).
Knowledge of MLOps, LLMOps, model deployment, monitoring, and lifecycle management.
Experience with distributed systems, APIs, microservices, and event-driven architectures.
Strong architecture modelling, design documentation, and governance experience.
Understanding of Responsible AI, AI security, and regulatory considerations.
Excellent stakeholder management and communication skills.
Preferred Technical Skills
Google Cloud Platform (Vertex AI, BigQuery, GCS).
Python and AI/ML ecosystems.
LangGraph, LangChain, ADK, orchestration frameworks.
Vector databases and Retrieval-Augmented Generation (RAG).
Kubernetes, Docker, CI/CD, DevOps practices.
Data architecture and data engineering concepts.
Enterprise integration and API management.
Leadership Expectations
Act as a recognised technical leader within the AI architecture community.
Mentor Solution Architects, Engineers, and Data Scientists.
Drive adoption of architecture standards and best practices.
Promote innovation while maintaining solid governance and risk management.
Influence strategic technology decisions through thought leadership.
Success Measures
Delivery of scalable and secure AI solutions.
Adoption of reusable architecture patterns.
Improved time-to-market for AI products.
Compliance with architecture, risk, and governance standards.
Positive business outcomes through AI-enabled capabilities

📌 Director of Artificial Intelligence (Hyderabad)
🏢 Xander Talent
📍 Hyderabad