AI Solution Architect

Grade - E1/E2

Job Description

We are seeking a Director level executive for Lead AI Architectwith proven expertise inLarge Language Models (LLMs), Agentic AI, LangChain, LangGraph, embeddings, vector databases, and semantic search. The role demandscloud expertise (Azure or GCP),application architecture knowledge, and experience indesigning and delivering enterprise-grade AI applicationsusingLLMs and agentic workflows.

The ideal candidate blendstechnical excellencewith aproduct mindset, capable of applyingdesign patterns in LangChain and LangGraph, as well asintegration architecture patterns, to deliver scalable, reusable, and business-aligned AI solutions.

Key Responsibilities

Overall experience: 15+ years of IT experience and strong experience with Data architectures

• AI Product Strategy & Delivery
• Enterprise AI & Application Architecture
• LLM & Agentic AI Development
• Embeddings, Vector Databases & RAG
• Cloud & MLOps
• Leadership & Hands-On Execution
• Lead theend-to-end AI product lifecycle- ideation, prototyping, MVP, and enterprise-grade rollout.
• Ensure all AI solutions arealigned with business KPIs, adoption metrics, and ROI goals.
• Collaborate withproduct, business, and architecture teamsto shape AI-enabled solutions.
• Architectscalable enterprise AI applicationsusing LLMs, embeddings, and agentic workflows.
• Applyintegration design patterns(e.g., API Gateway, event-driven, CQRS, pub-sub, orchestration vs. choreography) for seamless enterprise system integration.
• Ensuresecurity, compliance, and resiliencein all deployed solutions.
• Design and implementLLM-powered applications(OpenAI, Gemini, Claude, LLaMA).
• UseLangChain design patterns(e.g., Retrieval Chain, Conversational Chain, Sequential Chains, Tool-Using Agents) to create modular and reusable workflows.
• ApplyLangGraph design patternsformulti-agent orchestration, tool invocation, and branching logicin complex workflows.
• Implementagentic AI architectureswithModel Context Protocol (MCP)for adaptive and autonomous decision-making.
• Build and optimizeembedding pipelines(OpenAI Embeddings, SentenceTransformers, custom models).
• Architectsemantic search and RAG pipelineswith vector databases (FAISS, Weaviate, Pinecone, Milvus).
• Ensurelow-latency, scalable retrievalfor enterprise knowledge applications.
• Deploy AI workloads onAzure AI (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry)orGoogle Cloud AI (Vertex AI, Gemini, PaLM).
• LeadMLOps practicesfor automated training, CI/CD, monitoring, and retraining.
• Applycloud-native design patterns(e.g., sidecar, adapter, strangler fig, service mesh) for AI deployment.
• Be ahands-on leader, actively coding prototypes and leading architecture reviews.
• Mentor teams inLangChain/LangGraph design patterns, embeddings, RAG, and AI solution design.
• Drivebest practices in AI development, integration design, and scalable deployment.

Primary skills

• 8+ yearsin AI/ML development, with3+ yearsin enterprise LLM/agentic AI solutions.
• Hands-on expertise withLangChain, LangGraph, anddesign patterns for workflow orchestration.
• Deep understanding ofagentic AI architectures, embeddings, RAG pipelines, and semantic search.
• Strong Python development skills and experience integrating AI models withenterprise APIs and microservices.
• Experience withAzure AI or GCP AI servicesfor enterprise-scale deployments.
• Knowledge ofapplication and integration design patternsfor cloud-native architectures.
• Proficiency withvector databases(FAISS, Pinecone, Weaviate, Milvus).
• Experience withMLOps pipelines, CI/CD, and Kubernetes.
• Strongstakeholder management, communication, and leadershipskills