GenAI Solutions Architect

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

- Solid 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.

Preferred Experience

- Experience delivering multiple enterprise GenAI solutions from PoC to production.

- Exposure to multimodal AI, knowledge graphs, fine-tuning/model customization and advanced retrieval techniques.

- Experience with Docker, Kubernetes, CI/CD, Infrastructure as Code and observability platforms.

- Cloud or architecture certifications across Azure, AWS, GCP or TOGAF are advantageous.

Experience & Qualifications

- 12-15 years of overall experience in software engineering, AI/ML, cloud or solution architecture.

- 3+ years of hands-on experience with Generative AI / LLM-based solutions preferred.

- Proven experience operating as a Solution Architect, Technical Architect, GenAI Lead or equivalent.

- Bachelors or Masters degree in Computer Science, Engineering, AI or a related discipline.

Compensation -

Best in the industry, commensurate to experience and skills of the aspirant.

📌 GenAI Architect (Noida)
🏢 IRIS SOFTWARE
📍 Noida