Job Summary
Key Responsibilities Cross-team Architecture & Technical Leadership Architect and own the end-to-end Solution Design, working alongside a System Architect or alone for an Industry AI Solution Team, ensuring architectural consistency, pattern reuse, and alignment with enterprise standards. Serve as the primary technical authority for GenAI and Agentic AI architecture decisions within assigned teams, providing direction to embedded GenAI Developers and guiding their implementation work. Define and enforce architecture standards, design patterns, reference architectures, and guardrails for GenAI and Agentic AI development across teams. Lead architecture reviews, proof-of-concept evaluations, and technical design discussions for AI features and components. Generative AI & Agentic AI Solution Design Design scalable, production-grade GenAI application architectures including Retrieval-Augmented Generation (RAG) pipelines, Agentic Workflows, LLM integration layers, prompt management systems, and response evaluation frameworks. Architect multi-agent systems and Agentic AI workflows: agent orchestration patterns, tool use, memory management, long-horizon task execution, and human-in-the-loop designs. Select and evaluate appropriate LLMs, embedding models, and AI frameworks (commercial and open-source) based on performance, cost, latency, and compliance requirements. Define LLM fine-tuning and adaptation strategies (RLHF, PEFT, LoRA, prompt tuning) where required, overseeing implementation by the developer team. Design LLMOps and AI observability pipelines — including evaluation frameworks, tracing, monitoring, and feedback loops for deployed AI systems. Enterprise System Architecture Apply deep system architecture expertise to ensure GenAI solutions integrate seamlessly into existing enterprise platforms including CRM, ERP, workflow automation tools, and analytics systems. Design robust, secure API layers, microservices, and event-driven integration patterns that connect AI components with enterprise backends. Ensure AI architectures meet enterprise non-functional requirements: scalability, availability, latency, security, and disaster recovery. Collaborate with infrastructure and cloud teams to architect AI workloads on cloud platforms (Azure preferred: Azure OpenAI, Azure AI Studio, Azure ML) with appropriate cost and performance optimisation. Data Engineering & AI Data Architecture Design and govern data pipelines supporting AI model inference and (where applicable) fine-tuning: ingestion, transformation, quality validation, and versioning. Define data architecture for AI: vector databases, knowledge graphs, embedding stores, and structured/unstructured data integration patterns. Collaborate with data engineering teams to ensure data pipelines meet quality, freshness, and compliance requirements for AI use cases. Responsible AI, Governance & Security Embed AI fairness, transparency, explainability, and accountability principles into so
Key Responsibilities
2. To train and develop team so as to ensure that there is an adequate supply of trained manpower in the said technology and delivery risks are mitigated.
3. To ensure knowledge up-gradation and work with new technologies so that the solution is current and meets quality standards and the client requirements.
4. To gather specifications and deliver solutions to the client organization based on understanding of a domain or technology.