AI Solution Architect | 15+ Years exp | Gurgaon
Capgemini
AI Solution Architect | 15+ Years exp | Gurgaon
Job Description
AI Solution Architect | 15+ Years exp | Gurgaon
📋 Job Overview
Capgemini is seeking a Director-level Lead AI Architect with 15+ years of IT experience to design and deliver enterprise-grade AI applications using LLMs and agentic workflows. The role involves leading the end-to-end AI product lifecycle, architecting scalable solutions with LangChain and LangGraph, and ensuring alignment with business KPIs. The ideal candidate combines technical expertise in cloud platforms like Azure or GCP with strong leadership and a product mindset to drive innovative, reusable AI solutions.
📍 Location: Gurgaon
💼 Experience Level: Executives
🏢 Business Unit: I and D Global Business Line
🎯 Key Responsibilities
- Lead the end-to-end AI product lifecycle — ideation, prototyping, MVP, and enterprise-grade rollout
- Ensure all AI solutions are aligned with business KPIs, adoption metrics, and ROI goals
- Collaborate with product, business, and architecture teams to shape AI-enabled solutions
- Architect scalable enterprise AI applications using LLMs, embeddings, and agentic workflows
- Apply integration design patterns (e.g., API Gateway, event-driven, CQRS, pub-sub, orchestration vs. choreography) for seamless enterprise system integration
- Ensure security, compliance, and resilience in all deployed solutions
- Design and implement LLM-powered applications (OpenAI, Gemini, Claude, LLaMA)
- Use LangChain design patterns (e.g., Retrieval Chain, Conversational Chain, Sequential Chains, Tool-Using Agents) to create modular and reusable workflows
- Apply LangGraph design patterns for multi-agent orchestration, tool invocation, and branching logic in complex workflows
- Implement agentic AI architectures with Model Context Protocol (MCP) for adaptive and autonomous decision-making
- Build and optimize embedding pipelines (OpenAI Embeddings, SentenceTransformers, custom models)
- Architect semantic search and RAG pipelines with vector databases (FAISS, Weaviate, Pinecone, Milvus)
- Ensure low-latency, scalable retrieval for enterprise knowledge applications
- Deploy AI workloads on Azure AI (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry) or Google Cloud AI (Vertex AI, Gemini, PaLM)
- Lead MLOps practices for automated training, CI/CD, monitoring, and retraining
- Apply cloud-native design patterns (e.g., sidecar, adapter, strangler fig, service mesh) for AI deployment
- Be a hands-on leader, actively coding prototypes and leading architecture reviews
- Mentor teams in LangChain/LangGraph design patterns, embeddings, RAG, and AI solution design
- Drive best practices in AI development, integration design, and scalable deployment
✅ Required Qualifications
- 15+ years of IT experience
- Strong experience with Data architectures
- 8+ years in AI/ML development
- 3+ years in enterprise LLM/agentic AI solutions
- Director level executive
🛠️ Required Skills
- Large Language Models (LLMs)
- Agentic AI
- LangChain
- LangGraph
- Embeddings
- Vector databases
- Semantic search
- Cloud expertise (Azure or GCP)
- Application architecture
- Designing and delivering enterprise-grade AI applications
- Integration architecture patterns
- Python development
- Integrating AI models with enterprise APIs and microservices
- Azure AI services (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry)
- Google Cloud AI services (Vertex AI, Gemini, PaLM)
- MLOps pipelines
- CI/CD
- Kubernetes
- Design patterns for workflow orchestration
- Agentic AI architectures
- RAG pipelines
- OpenAI
- Gemini
- Claude
- LLaMA
- Retrieval Chain
- Conversational Chain
- Sequential Chains
- Tool-Using Agents
- Multi-agent orchestration
- Tool invocation
- Branching logic
- Model Context Protocol (MCP)
- OpenAI Embeddings
- SentenceTransformers
- Custom models
- FAISS
- Weaviate
- Pinecone
- Milvus
- API Gateway
- Event-driven
- CQRS
- Pub-sub
- Orchestration vs. choreography
- Sidecar
- Adapter
- Strangler fig
- Service mesh
- Stakeholder management
- Communication
- Leadership
Locations
- Gurgaon, India
Salary
Estimated Salary Rangemedium confidence
3,500,000 - 5,500,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Large Language Models (LLMs)intermediate
- Agentic AIintermediate
- LangChainintermediate
- LangGraphintermediate
- Embeddingsintermediate
- Vector databasesintermediate
- Semantic searchintermediate
- Cloud expertise (Azure or GCP)intermediate
- Application architectureintermediate
- Designing and delivering enterprise-grade AI applicationsintermediate
- Integration architecture patternsintermediate
- Python developmentintermediate
- Integrating AI models with enterprise APIs and microservicesintermediate
- Azure AI services (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry)intermediate
- Google Cloud AI services (Vertex AI, Gemini, PaLM)intermediate
- MLOps pipelinesintermediate
- CI/CDintermediate
- Kubernetesintermediate
- Design patterns for workflow orchestrationintermediate
- Agentic AI architecturesintermediate
- RAG pipelinesintermediate
- OpenAIintermediate
- Geminiintermediate
- Claudeintermediate
- LLaMAintermediate
- Retrieval Chainintermediate
- Conversational Chainintermediate
- Sequential Chainsintermediate
- Tool-Using Agentsintermediate
- Multi-agent orchestrationintermediate
- Tool invocationintermediate
- Branching logicintermediate
- Model Context Protocol (MCP)intermediate
- OpenAI Embeddingsintermediate
- SentenceTransformersintermediate
- Custom modelsintermediate
- FAISSintermediate
- Weaviateintermediate
- Pineconeintermediate
- Milvusintermediate
- API Gatewayintermediate
- Event-drivenintermediate
- CQRSintermediate
- Pub-subintermediate
- Orchestration vs. choreographyintermediate
- Sidecarintermediate
- Adapterintermediate
- Strangler figintermediate
- Service meshintermediate
- Stakeholder managementintermediate
- Communicationintermediate
- Leadershipintermediate
Required Qualifications
- 15+ years of IT experience (experience)
- Strong experience with Data architectures (experience)
- 8+ years in AI/ML development (experience)
- 3+ years in enterprise LLM/agentic AI solutions (experience)
- Director level executive (experience)
Responsibilities
- Lead the end-to-end AI product lifecycle — ideation, prototyping, MVP, and enterprise-grade rollout
- Ensure all AI solutions are aligned with business KPIs, adoption metrics, and ROI goals
- Collaborate with product, business, and architecture teams to shape AI-enabled solutions
- Architect scalable enterprise AI applications using LLMs, embeddings, and agentic workflows
- Apply integration design patterns (e.g., API Gateway, event-driven, CQRS, pub-sub, orchestration vs. choreography) for seamless enterprise system integration
- Ensure security, compliance, and resilience in all deployed solutions
- Design and implement LLM-powered applications (OpenAI, Gemini, Claude, LLaMA)
- Use LangChain design patterns (e.g., Retrieval Chain, Conversational Chain, Sequential Chains, Tool-Using Agents) to create modular and reusable workflows
- Apply LangGraph design patterns for multi-agent orchestration, tool invocation, and branching logic in complex workflows
- Implement agentic AI architectures with Model Context Protocol (MCP) for adaptive and autonomous decision-making
- Build and optimize embedding pipelines (OpenAI Embeddings, SentenceTransformers, custom models)
- Architect semantic search and RAG pipelines with vector databases (FAISS, Weaviate, Pinecone, Milvus)
- Ensure low-latency, scalable retrieval for enterprise knowledge applications
- Deploy AI workloads on Azure AI (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry) or Google Cloud AI (Vertex AI, Gemini, PaLM)
- Lead MLOps practices for automated training, CI/CD, monitoring, and retraining
- Apply cloud-native design patterns (e.g., sidecar, adapter, strangler fig, service mesh) for AI deployment
- Be a hands-on leader, actively coding prototypes and leading architecture reviews
- Mentor teams in LangChain/LangGraph design patterns, embeddings, RAG, and AI solution design
- Drive best practices in AI development, integration design, and scalable deployment
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AI Solution Architect | 15+ Years exp | Gurgaon
Capgemini
AI Solution Architect | 15+ Years exp | Gurgaon
Job Description
AI Solution Architect | 15+ Years exp | Gurgaon
📋 Job Overview
Capgemini is seeking a Director-level Lead AI Architect with 15+ years of IT experience to design and deliver enterprise-grade AI applications using LLMs and agentic workflows. The role involves leading the end-to-end AI product lifecycle, architecting scalable solutions with LangChain and LangGraph, and ensuring alignment with business KPIs. The ideal candidate combines technical expertise in cloud platforms like Azure or GCP with strong leadership and a product mindset to drive innovative, reusable AI solutions.
📍 Location: Gurgaon
💼 Experience Level: Executives
🏢 Business Unit: I and D Global Business Line
🎯 Key Responsibilities
- Lead the end-to-end AI product lifecycle — ideation, prototyping, MVP, and enterprise-grade rollout
- Ensure all AI solutions are aligned with business KPIs, adoption metrics, and ROI goals
- Collaborate with product, business, and architecture teams to shape AI-enabled solutions
- Architect scalable enterprise AI applications using LLMs, embeddings, and agentic workflows
- Apply integration design patterns (e.g., API Gateway, event-driven, CQRS, pub-sub, orchestration vs. choreography) for seamless enterprise system integration
- Ensure security, compliance, and resilience in all deployed solutions
- Design and implement LLM-powered applications (OpenAI, Gemini, Claude, LLaMA)
- Use LangChain design patterns (e.g., Retrieval Chain, Conversational Chain, Sequential Chains, Tool-Using Agents) to create modular and reusable workflows
- Apply LangGraph design patterns for multi-agent orchestration, tool invocation, and branching logic in complex workflows
- Implement agentic AI architectures with Model Context Protocol (MCP) for adaptive and autonomous decision-making
- Build and optimize embedding pipelines (OpenAI Embeddings, SentenceTransformers, custom models)
- Architect semantic search and RAG pipelines with vector databases (FAISS, Weaviate, Pinecone, Milvus)
- Ensure low-latency, scalable retrieval for enterprise knowledge applications
- Deploy AI workloads on Azure AI (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry) or Google Cloud AI (Vertex AI, Gemini, PaLM)
- Lead MLOps practices for automated training, CI/CD, monitoring, and retraining
- Apply cloud-native design patterns (e.g., sidecar, adapter, strangler fig, service mesh) for AI deployment
- Be a hands-on leader, actively coding prototypes and leading architecture reviews
- Mentor teams in LangChain/LangGraph design patterns, embeddings, RAG, and AI solution design
- Drive best practices in AI development, integration design, and scalable deployment
✅ Required Qualifications
- 15+ years of IT experience
- Strong experience with Data architectures
- 8+ years in AI/ML development
- 3+ years in enterprise LLM/agentic AI solutions
- Director level executive
🛠️ Required Skills
- Large Language Models (LLMs)
- Agentic AI
- LangChain
- LangGraph
- Embeddings
- Vector databases
- Semantic search
- Cloud expertise (Azure or GCP)
- Application architecture
- Designing and delivering enterprise-grade AI applications
- Integration architecture patterns
- Python development
- Integrating AI models with enterprise APIs and microservices
- Azure AI services (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry)
- Google Cloud AI services (Vertex AI, Gemini, PaLM)
- MLOps pipelines
- CI/CD
- Kubernetes
- Design patterns for workflow orchestration
- Agentic AI architectures
- RAG pipelines
- OpenAI
- Gemini
- Claude
- LLaMA
- Retrieval Chain
- Conversational Chain
- Sequential Chains
- Tool-Using Agents
- Multi-agent orchestration
- Tool invocation
- Branching logic
- Model Context Protocol (MCP)
- OpenAI Embeddings
- SentenceTransformers
- Custom models
- FAISS
- Weaviate
- Pinecone
- Milvus
- API Gateway
- Event-driven
- CQRS
- Pub-sub
- Orchestration vs. choreography
- Sidecar
- Adapter
- Strangler fig
- Service mesh
- Stakeholder management
- Communication
- Leadership
Locations
- Gurgaon, India
Salary
Estimated Salary Rangemedium confidence
3,500,000 - 5,500,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Large Language Models (LLMs)intermediate
- Agentic AIintermediate
- LangChainintermediate
- LangGraphintermediate
- Embeddingsintermediate
- Vector databasesintermediate
- Semantic searchintermediate
- Cloud expertise (Azure or GCP)intermediate
- Application architectureintermediate
- Designing and delivering enterprise-grade AI applicationsintermediate
- Integration architecture patternsintermediate
- Python developmentintermediate
- Integrating AI models with enterprise APIs and microservicesintermediate
- Azure AI services (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry)intermediate
- Google Cloud AI services (Vertex AI, Gemini, PaLM)intermediate
- MLOps pipelinesintermediate
- CI/CDintermediate
- Kubernetesintermediate
- Design patterns for workflow orchestrationintermediate
- Agentic AI architecturesintermediate
- RAG pipelinesintermediate
- OpenAIintermediate
- Geminiintermediate
- Claudeintermediate
- LLaMAintermediate
- Retrieval Chainintermediate
- Conversational Chainintermediate
- Sequential Chainsintermediate
- Tool-Using Agentsintermediate
- Multi-agent orchestrationintermediate
- Tool invocationintermediate
- Branching logicintermediate
- Model Context Protocol (MCP)intermediate
- OpenAI Embeddingsintermediate
- SentenceTransformersintermediate
- Custom modelsintermediate
- FAISSintermediate
- Weaviateintermediate
- Pineconeintermediate
- Milvusintermediate
- API Gatewayintermediate
- Event-drivenintermediate
- CQRSintermediate
- Pub-subintermediate
- Orchestration vs. choreographyintermediate
- Sidecarintermediate
- Adapterintermediate
- Strangler figintermediate
- Service meshintermediate
- Stakeholder managementintermediate
- Communicationintermediate
- Leadershipintermediate
Required Qualifications
- 15+ years of IT experience (experience)
- Strong experience with Data architectures (experience)
- 8+ years in AI/ML development (experience)
- 3+ years in enterprise LLM/agentic AI solutions (experience)
- Director level executive (experience)
Responsibilities
- Lead the end-to-end AI product lifecycle — ideation, prototyping, MVP, and enterprise-grade rollout
- Ensure all AI solutions are aligned with business KPIs, adoption metrics, and ROI goals
- Collaborate with product, business, and architecture teams to shape AI-enabled solutions
- Architect scalable enterprise AI applications using LLMs, embeddings, and agentic workflows
- Apply integration design patterns (e.g., API Gateway, event-driven, CQRS, pub-sub, orchestration vs. choreography) for seamless enterprise system integration
- Ensure security, compliance, and resilience in all deployed solutions
- Design and implement LLM-powered applications (OpenAI, Gemini, Claude, LLaMA)
- Use LangChain design patterns (e.g., Retrieval Chain, Conversational Chain, Sequential Chains, Tool-Using Agents) to create modular and reusable workflows
- Apply LangGraph design patterns for multi-agent orchestration, tool invocation, and branching logic in complex workflows
- Implement agentic AI architectures with Model Context Protocol (MCP) for adaptive and autonomous decision-making
- Build and optimize embedding pipelines (OpenAI Embeddings, SentenceTransformers, custom models)
- Architect semantic search and RAG pipelines with vector databases (FAISS, Weaviate, Pinecone, Milvus)
- Ensure low-latency, scalable retrieval for enterprise knowledge applications
- Deploy AI workloads on Azure AI (Azure OpenAI, Azure Cognitive Search, Azure ML, Foundry) or Google Cloud AI (Vertex AI, Gemini, PaLM)
- Lead MLOps practices for automated training, CI/CD, monitoring, and retraining
- Apply cloud-native design patterns (e.g., sidecar, adapter, strangler fig, service mesh) for AI deployment
- Be a hands-on leader, actively coding prototypes and leading architecture reviews
- Mentor teams in LangChain/LangGraph design patterns, embeddings, RAG, and AI solution design
- Drive best practices in AI development, integration design, and scalable deployment
Tags & Categories
Answer 10 quick questions to check your fit for AI Solution Architect | 15+ Years exp | Gurgaon @ Capgemini.
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~2 Minutes
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Related Books and Jobs