Gen AI Fullstack - Technical Architect

Mandatory Skills:

Role Purpose

• Lead the architecture and delivery of enterprise-grade Generative AI and Agentic AI solutions, translating business requirements into scalable, secure, and production-ready AI platforms.
• Own the technical vision from discovery and PoC through production deployment and optimization.

Key Responsibilities

• Design end-to-end GenAI, RAG, Agentic AI and multi-agent architectures.
• Define solution patterns covering LLMs, embeddings, vector databases, orchestration, APIs, data pipelines and enterprise integrations.
• Architect AI agents with tool/function calling, workflow orchestration, memory, Human-in-the-Loop and MCP-based integrations.
• Evaluate and select appropriate foundation models, AI platforms and architecture patterns based on quality, latency, security and cost.
• Design solutions using Azure OpenAI / Azure AI Foundry, Amazon Bedrock / AgentCore, Google Vertex AI or equivalent platforms.
• Establish LLMOps, evaluation, observability, guardrails and Responsible AI practices.
• Define scalable cloud-native deployment architectures using APIs, containers, Kubernetes, serverless and asynchronous/event-driven patterns.
• Ensure architecture meets enterprise requirements for security, privacy, resilience, scalability, compliance and cost optimization.
• Lead architecture workshops, technical design reviews, PoCs and solution demonstrations.
• Create reference architectures, technical standards and reusable accelerators for GenAI adoption.
• Partner with business, product, security, data and engineering stakeholders to drive solutions from use case identification to production adoption.

Required Skills

• Strong expertise in Generative AI, LLMs, Agentic AI and AI solution architecture.
• Hands-on experience with RAG, embeddings, vector search and semantic retrieval.
• Experience with agent frameworks such as LangGraph, LangChain, Microsoft Agent Framework, Semantic Kernel, Bedrock Agents/AgentCore or equivalent.
• Strong understanding of multi-agent orchestration, tool calling, context engineering, memory and Human-in-the-Loop patterns.
• Experience with one or more cloud AI ecosystems: Azure, AWS or GCP.
• Strong knowledge of Python, REST APIs, microservices and distributed/cloud-native architectures.
• Experience with LLM evaluation, tracing, monitoring, prompt/model versioning and LLMOps/MLOps.
• Understanding of AI security, guardrails, Responsible AI, identity/access management and data governance.
• Ability to assess architectural trade-offs across accuracy, latency, scalability, reliability and cost.