GenAI Solutions Architect

About the Role

We are looking for a highly skilled and innovative GenAI Solutions Architect with 6–9 years of experience in Data & AI, Generative AI, and Agentic AI. The ideal candidate will design, architect, and lead enterprise-grade AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and cloud-native AI services. This role requires strong technical depth, customer-facing expertise, and the ability to translate business challenges into scalable AI architectures.

Key Responsibilities

• Design end-to-end architectures for Generative AI and Agentic AI solutions across industries.
• Build scalable LLM-based solutions using RAG pipelines, vector databases, embeddings, and orchestration frameworks.
• Architect AI agents and multi-agent workflows for automation, reasoning, and decision support systems.
• Lead technical discovery sessions with customers to understand business use cases and define AI transformation roadmaps.
• Collaborate with Solution Sales, Data Engineers, and Cloud teams to develop Proof-of-Concepts (POCs) and MVPs.
• Ensure best practices in AI governance, security, data privacy, model monitoring, and responsible AI implementation.
• Optimize performance, cost, and scalability of AI workloads on AWS, Azure, or GCP.
• Stay updated with advancements in multimodal AI, fine-tuning, model evaluation, and AI observability tools.

Qualifications & Experience

• 6–9 years of experience in Data Engineering, AI/ML, or Cloud Architecture.
• Hands-on experience with Generative AI, LLMs, Prompt Engineering, and Agentic AI frameworks.
• Strong knowledge of RAG architectures, vector databases (Pinecone, FAISS, etc.), and embeddings.
• Experience working with cloud AI services – AWS Bedrock, Azure OpenAI, Azure AI Foundry, Copilot Studio, Google Vertex AI, Google BigQuery, Gemini Enterprise.
• Proficiency in Python and AI frameworks such as LangChain, LangGraph, Agno, Pydantic AI agents CrewAI, Google ADK, AWS Strands agents/Bedrock Agentcore, Microsoft Agent Framework, LlamaIndex, or similar orchestration tools.
• Strong understanding of MLOps, CI/CD pipelines, containerization (Docker/Kubernetes), and API integrations.
• Experience in stakeholder engagement and presenting technical solutions to enterprise customers.

Key Skills

• GenAI Architecture & Design
• Agentic AI & Multi-Agent Systems
• RAG Pipelines & Vector Search
• Cloud AI Platforms (AWS, Azure, GCP)
• LLM Fine-tuning & Evaluation
• AI Governance & Responsible AI
• Stakeholder Communication & Technical Leadership