Your key responsibilities Technical Excellence
Location: Bangalore, Working from office Mandatory. Experience Level: 6-8 years Job Summary We are seeking a passionate and highly skilled Solution Architect Generative AI & Agentic AI to design, develop, and deploy intelligent systems leveraging large language models (LLMs), multiagent frameworks, and enterprise AI integration patterns. This role will play a key part in building next-generation AI applications that autonomously reason, act, and learn across complex business workflows. As a Generative?&?Agentic?AI?Solution Architect you will own the end-to-end architecture for enterprise-grade GenAI solutions and multi-agent systems. Youll translate business objectives into scalable, compliant, and observable AI capabilitiesspanning data pipelines, foundation-model ops, Retrieval-Augmented?Generation (RAG), and autonomous agent orchestration. You will partner with product owners, data scientists, platform engineers, security, and business stakeholders to accelerate AI-driven transformation while safeguarding performance, ethics, and regulatory compliance. Key Responsibilities: Solution Development:
Design and implement applications using LLMs (e.g., OpenAI, Mistral, Claude, Llama). Build autonomous multi-agent systems using frameworks like AutoGen, LangGraph, CrewAI, or AgentOps. Develop scalable backend services and orchestration pipelines using Python/Node.js integrated with AI APIs. Lead discovery workshops, create MVP backlogs, and convert PoCs into production-ready solutions following MLOps / LLMOps best practices. Oversee model selection, finetuning, prompt-engineering, evaluation, and continuous monitoring. Architecture & Engineering: Translate business requirements into agentic workflows with reasoning, memory, and tool usage. Integrate GenAI and agents with enterprise systems (e.g., SAP, ServiceNow, Salesforce) via APIs and RAG pipelines. Work with vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and long-term memory. Define reference architectures for GenAI (LLMs, Diffusion, SSMs) and agentic patterns (task-decomposition, planner-executor, tool-calling). Design secure micro-service and event-driven topologies on cloud / hybrid infra (AWS Bedrock, Azure OpenAI, Google Vertex, private GPUs). Required?Technical?Competencies: Generative?AI: Hands-on with LLMs (GPT-4o, Claude-3, Gemini, Llama-3), Diffusion, and audio-/vision models; experience with HF Transformers, LangChain / LlamaIndex. Agentic?Frameworks: Experience designing goal-oriented agents using AutoGen, CrewAI, Semantic?Kernel, or custom planners; familiarity with tool-calling, memory management, and AI?schedulers. LLMOps / MLOps: CI/CD for model artifacts, feature stores, vector DBs (Pinecone, Weaviate, FAISS), model gateways, and policy engines (Guardrails, Azure?AI?Safety). Cloud & DevSecOps: Terraform, Kubernetes, Docker, serverless, GPU orchestration, secret management, SAST/DAST integration. Programming: Python (primary), TypeScript/Node, Bash; strong design-pattern discipline and code-review leadership. Build replaceable but working proof-of-conceptsCLI, Streamlit, or VS Code Jupyterso stakeholders can touch the idea. Write production-grade Python/TypeScript to orchestrate agents with AutoGen / CrewAI / Semantic?Kernel. Hook agents to real systems (SAP BAPIs, Salesforce APIs, REST/GraphQL) and manage auth tokens & secrets. Build CI/CD (GitHub?Actions, Terraform, Helm) that auto-tests prompts, pushes Docker images, and provisions GPUs. Instrument tracing (OpenTelemetry) and set up dashboards for tokens, latency, cost per call. Education: Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
Skills and attributes
To qualify for the role you must have
Qualification
Location: Bangalore, Working from office Mandatory. Experience Level: 6-8 years Job Summary We are seeking a passionate and highly skilled Solution Architect Generative AI & Agentic AI to design, develop, and deploy intelligent systems leveraging large language models (LLMs), multiagent frameworks, and enterprise AI integration patterns. This role will play a key part in building next-generation AI applications that autonomously reason, act, and learn across complex business workflows. As a Generative?&?Agentic?AI?Solution Architect you will own the end-to-end architecture for enterprise-grade GenAI solutions and multi-agent systems. Youll translate business objectives into scalable, compliant, and observable AI capabilitiesspanning data pipelines, foundation-model ops, Retrieval-Augmented?Generation (RAG), and autonomous agent orchestration. You will partner with product owners, data scientists, platform engineers, security, and business stakeholders to accelerate AI-driven transformation while safeguarding performance, ethics, and regulatory compliance. Key Responsibilities: Solution Development:
Design and implement applications using LLMs (e.g., OpenAI, Mistral, Claude, Llama). Build autonomous multi-agent systems using frameworks like AutoGen, LangGraph, CrewAI, or AgentOps. Develop scalable backend services and orchestration pipelines using Python/Node.js integrated with AI APIs. Lead discovery workshops, create MVP backlogs, and convert PoCs into production-ready solutions following MLOps / LLMOps best practices. Oversee model selection, finetuning, prompt-engineering, evaluation, and continuous monitoring. Architecture & Engineering: Translate business requirements into agentic workflows with reasoning, memory, and tool usage. Integrate GenAI and agents with enterprise systems (e.g., SAP, ServiceNow, Salesforce) via APIs and RAG pipelines. Work with vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and long-term memory. Define reference architectures for GenAI (LLMs, Diffusion, SSMs) and agentic patterns (task-decomposition, planner-executor, tool-calling). Design secure micro-service and event-driven topologies on cloud / hybrid infra (AWS Bedrock, Azure OpenAI, Google Vertex, private GPUs). Required?Technical?Competencies: Generative?AI: Hands-on with LLMs (GPT-4o, Claude-3, Gemini, Llama-3), Diffusion, and audio-/vision models; experience with HF Transformers, LangChain / LlamaIndex. Agentic?Frameworks: Experience designing goal-oriented agents using AutoGen, CrewAI, Semantic?Kernel, or custom planners; familiarity with tool-calling, memory management, and AI?schedulers. LLMOps / MLOps: CI/CD for model artifacts, feature stores, vector DBs (Pinecone, Weaviate, FAISS), model gateways, and policy engines (Guardrails, Azure?AI?Safety). Cloud & DevSecOps: Terraform, Kubernetes, Docker, serverless, GPU orchestration, secret management, SAST/DAST integration. Programming: Python (primary), TypeScript/Node, Bash; strong design-pattern discipline and code-review leadership. Build replaceable but working proof-of-conceptsCLI, Streamlit, or VS Code Jupyterso stakeholders can touch the idea. Write production-grade Python/TypeScript to orchestrate agents with AutoGen / CrewAI / Semantic?Kernel. Hook agents to real systems (SAP BAPIs, Salesforce APIs, REST/GraphQL) and manage auth tokens & secrets. Build CI/CD (GitHub?Actions, Terraform, Helm) that auto-tests prompts, pushes Docker images, and provisions GPUs. Instrument tracing (OpenTelemetry) and set up dashboards for tokens, latency, cost per call. Education: Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field. Experience
Location: Bangalore, Working from office Mandatory. Experience Level: 6-8 years Job Summary We are seeking a passionate and highly skilled Solution Architect Generative AI & Agentic AI to design, develop, and deploy intelligent systems leveraging large language models (LLMs), multiagent frameworks, and enterprise AI integration patterns. This role will play a key part in building next-generation AI applications that autonomously reason, act, and learn across complex business workflows. As a Generative?&?Agentic?AI?Solution Architect you will own the end-to-end architecture for enterprise-grade GenAI solutions and multi-agent systems. Youll translate business objectives into scalable, compliant, and observable AI capabilitiesspanning data pipelines, foundation-model ops, Retrieval-Augmented?Generation (RAG), and autonomous agent orchestration. You will partner with product owners, data scientists, platform engineers, security, and business stakeholders to accelerate AI-driven transformation while safeguarding performance, ethics, and regulatory compliance. Key Responsibilities: Solution Development:
Design and implement applications using LLMs (e.g., OpenAI, Mistral, Claude, Llama). Build autonomous multi-agent systems using frameworks like AutoGen, LangGraph, CrewAI, or AgentOps. Develop scalable backend services and orchestration pipelines using Python/Node.js integrated with AI APIs. Lead discovery workshops, create MVP backlogs, and convert PoCs into production-ready solutions following MLOps / LLMOps best practices. Oversee model selection, finetuning, prompt-engineering, evaluation, and continuous monitoring. Architecture & Engineering: Translate business requirements into agentic workflows with reasoning, memory, and tool usage. Integrate GenAI and agents with enterprise systems (e.g., SAP, ServiceNow, Salesforce) via APIs and RAG pipelines. Work with vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and long-term memory. Define reference architectures for GenAI (LLMs, Diffusion, SSMs) and agentic patterns (task-decomposition, planner-executor, tool-calling). Design secure micro-service and event-driven topologies on cloud / hybrid infra (AWS Bedrock, Azure OpenAI, Google Vertex, private GPUs). Required?Technical?Competencies: Generative?AI: Hands-on with LLMs (GPT-4o, Claude-3, Gemini, Llama-3), Diffusion, and audio-/vision models; experience with HF Transformers, LangChain / LlamaIndex. Agentic?Frameworks: Experience designing goal-oriented agents using AutoGen, CrewAI, Semantic?Kernel, or custom planners; familiarity with tool-calling, memory management, and AI?schedulers. LLMOps / MLOps: CI/CD for model artifacts, feature stores, vector DBs (Pinecone, Weaviate, FAISS), model gateways, and policy engines (Guardrails, Azure?AI?Safety). Cloud & DevSecOps: Terraform, Kubernetes, Docker, serverless, GPU orchestration, secret management, SAST/DAST integration. Programming: Python (primary), TypeScript/Node, Bash; strong design-pattern discipline and code-review leadership. Build replaceable but working proof-of-conceptsCLI, Streamlit, or VS Code Jupyterso stakeholders can touch the idea. Write production-grade Python/TypeScript to orchestrate agents with AutoGen / CrewAI / Semantic?Kernel. Hook agents to real systems (SAP BAPIs, Salesforce APIs, REST/GraphQL) and manage auth tokens & secrets. Build CI/CD (GitHub?Actions, Terraform, Helm) that auto-tests prompts, pushes Docker images, and provisions GPUs. Instrument tracing (OpenTelemetry) and set up dashboards for tokens, latency, cost per call. Education: Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
What we look for
People with the ability to work in a collaborative manner to provide services across multiple client departments while following the commercial and legal requirements. You will need a practical approach to solving issues and complex problems with the ability to deliver insightful and practical solutions. We look for people who are agile, curious, mindful and able to sustain postivie energy, while being adaptable and creative in their approach.
What we offer
With more than 200,000 clients, 300,000 people globally and 33,000 people in India, EY has become the strongest brand and the most attractive employer in our field, with market-leading growth over compete. Our people work side-by-side with market-leading entrepreneurs, game- changers, disruptors and visionaries. As an organisation, we are investing more time, technology and money, than ever before in skills and learning for our people. At EY, you will have a personalized Career Journey and also the chance to tap into the resources of our career frameworks to better know about your roles, skills and opportunities.
EY is equally committed to being an inclusive employer and we strive to achieve the right balance for our people - enabling us to deliver excellent client service whilst allowing our people to build their career as well as focus on their wellbeing.
If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible.
Join us in building a better working world. Apply now.
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