Solution Architect - AI

9018 AccentureSolutionsPvtLtd. Company; Ernakulam; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Accenture is seeking a Solution Architect – AI with extensive experience in Generative AI, LLMs, and enterprise architecture. You will design end-to-end AI solutions across cloud and hybrid environments, working with stakeholders and security teams to ensure scalable, secure implementations.

The role requires 10+ years in technology and at least 3 years in AI-focused architecture, with hands-on cloud experience on AWS, Azure, or GCP. Location includes Indian tech hubs with multi-city engagement.

Qualifications

• 10+ years of overall technology experience
• At least 3 years designing/architecting AI solutions
• Strong background in enterprise solution architecture
• Experience on at least one major cloud platform (AWS/Azure/GCP)
• Knowledge of AI architecture patterns: Generative AI, LLMs, RAG, AI agents, vector DBs

Responsibilities

• Design end-to-end AI and Generative AI solution architectures
• Provide architectural leadership across the solution lifecycle
• Collaborate with business and tech stakeholders to define roadmaps
• Lead architecture reviews and design governance activities
• Define architectures for LLM-based applications and AI security, governance, and observability

Skills

Generative AI

Intelligent Automation

Conversational AI

Agentic AI

Large Language Models (LLMs)

AWS

Azure

GCP

Education

B.Tech/BE/M.Tech/MCA

Tools

AWS

Azure

GCP

Job description

Job Title – Solution Architect - AI – Manager - ACS SONG Management Level: Level 7 –Manager Location: Kochi, Coimbatore, Trivandrum, Bangalore Must have skills: Generative AI, Intelligent Automation, Conversational AI, or Agentic AI, , Large Language Models (LLMs) Good to have skills: Model Context Protocol (MCP)/ Agent2Agent (A2A) Experience: 10 - 15 years of experience is required Educational Qualification: B.Tech/BE/M.Tech/MCA

Job Summary We are seeking an experienced Solution Architect – AIwith10+ years of overall technology experience, including at least3 years of hands‑on experience designing and architecting AI, Generative AI, Machine Learning, or Agentic AI solutions. The ideal candidate will have a strong background in enterprise solution architecture and experience designing scalable, secure, resilient, and production‑ready AI solutions on at least one major cloud platform such asAWS, Microsoft Azure, or Google Cloud Platform (GCP). The role will be responsible for defining end‑to‑end AI solution architectures covering application, data, AI/ML, integration, security, infrastructure, and operational components. The architect will work closely with business stakeholders, enterprise architects, data teams, AI engineers, software engineers, security teams, and cloud platform teams to translate business requirements into practical and scalable technology solutions. The candidate should have strong knowledge of modern AI architecture patterns includingGenerative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, AI agents, vector databases, knowledge retrieval, API/tool integration, model orchestration, observability, security, and responsible AI. Experience in taking AI solutions from initial discovery and proof‑of‑concept through production deployment and operationalization is essential.

Roles and Responsibilities

Design end‑to‑endAI and Generative AI solution architecturesthat address business requirements while meeting enterprise standards for scalability, security, performance, reliability, and maintainability. Define appropriate architecture patterns forGenerative AI, Agentic AI, RAG, Machine Learning, intelligent automation, conversational AI, and AI‑enabled enterprise applications. Provide architectural leadership across the complete solution lifecycle, fromdiscovery, feasibility assessment, architecture definition, prototyping, implementation, production deployment, and post‑production operations. Collaborate with business and technology stakeholders to evaluate AI use cases, identify suitable technologies, assess technical feasibility, and define implementation roadmaps. Lead thearchitecture and solution designof enterprise AI, Generative AI, Machine Learning, and Agentic AI solutions across cloud and hybrid environments. Translate business requirements, functional requirements, and non‑functional requirements into scalable and securesolution architecture designs, architecture diagrams, integration patterns, and technical specifications. Design AI solutions using services and technologies available onAWS, Microsoft Azure, or Google Cloud Platform, including managed AI/ML, data, integration, compute, security, and observability services. Define architectures forLLM‑based applications, including prompt orchestration, Retrieval‑Augmented Generation (RAG), vector search, embeddings, model routing, grounding, knowledge bases, AI agents, tool/function calling, and multi‑agent architectures. Design enterprise integration patterns connecting AI solutions withAPIs, databases, data platforms, SaaS applications, enterprise applications, event‑driven systems, and external services. Evaluate and recommend appropriatefoundation models, LLMs, embedding models, AI platforms, vector databases, orchestration frameworks, and supporting technologiesbased on business, technical, security, cost, and performance requirements. Define architecture approaches forAI security, identity and access management, data privacy, guardrails, content filtering, responsible AI, model governance, and regulatory compliance. Establish architectural standards forAI observability, monitoring, tracing, logging, evaluation, performance management, reliability, and operational support. Work closely with AI engineers, data scientists, data engineers, application developers, DevOps/MLOps teams, cloud engineers, and security teams to guide implementation and ensure alignment with the target architecture. Lead architecture reviews, technical design workshops, proof‑of‑concepts, technology assessments, and design governance activities while communicating complex technical concepts effectively to both technical and business stakeholders. Ability to conductAI use‑case discovery, technical feasibility assessments, architecture assessments, technology evaluations, and solution option analysis. Ability to create and communicatehigh‑level architecture, detailed solution architecture, sequence diagrams, data flows, integration diagrams, deployment architectures, and architecture decision records. Ability to design appropriateAI guardrailsaddressing areas such as hallucination, prompt injection, data leakage, harmful content, unauthorized tool execution, and inappropriate model responses. Ability to definereference architectures, reusable architecture patterns, technical standards, and architecture governance frameworksfor AI solutions.

Professional and Technical Skills

10+ years of overall experiencein software engineering, application architecture, cloud architecture, data architecture, solution architecture, or related technology roles. Minimum3 years of experience designing or implementing AI‑based solutions, including Generative AI, Machine Learning, Intelligent Automation, Conversational AI, or Agentic AI. Demonstrated experience working as aSolution Architect, Technical Architect, Cloud Architect, AI Architect, or equivalent senior architecture role. Proven experience designing and deliveringenterprise‑scale, production‑grade solutionsinvolving multiple applications, platforms, integrations, and data sources. Hands‑on architecture experience withRetrieval‑Augmented Generation (RAG), embeddings, vector databases, semantic search, document retrieval, and enterprise knowledge bases. Strong hands‑on architecture experience with at least one major cloud platform: AWS– for example Amazon Bedrock, SageMaker, Microsoft Azure– for example Azure AI Foundry, Azure AI Search, Google Cloud Platform (GCP)– for example Vertex AI, Gemini, BigQuery Strong understanding ofcloud‑native architecture patterns, including microservices, APIs, serverless architectures, containers, event‑driven architectures, asynchronous processing, and distributed systems. Strong understanding ofREST APIs, API gateways, authentication and authorization, OAuth/OIDC, networking, secrets management, encryption, and enterprise security architecture. Strong understanding of enterprise data architectures includingdata lakes, data warehouses, lakehouses, relational databases, NoSQL databases, vector databases, and streaming platforms. Strong understanding of enterprise security architecture includingIdentity and Access Management, Role‑Based Access Control, network security, encryption, key management, secrets management, and secure API design. Strong ability to evaluate architectural trade‑offs acrosscost, performance, scalability, security, maintainability, complexity, and time to market. Experience integrating AI applications with enterprise data platforms such asDatabricks, Snowflake, BigQuery, Amazon Redshift, Microsoft Fabric, or equivalent technologies. Experience designing integrations between AI solutions and enterprise systems usingAPIs, messaging platforms, event buses, queues, workflow engines, and integration platforms. Experience designing highly available, scalable, fault‑tolerant, secure, and cost‑effective enterprise solutions. Experience taking technology solutions through multiple lifecycle stages includingdiscovery, architecture, proof‑of‑concept, implementation, deployment, and production support. Experience with prompt engineering, context management, grounding techniques, model selection, model evaluation, hallucination mitigation, and AI guardrails. Experience defining deployment strategies for AI applications across development, testing, staging, and production environments. Experience working with enterprise security, risk, compliance, and governance teams to ensure AI solutions meet organizational and regulatory requirements. Experience estimating solution complexity, cloud infrastructure requirements, implementation effort, technical dependencies, and architecture risks. Understanding of common AI orchestration and development frameworks such asLangChain, LangGraph, Semantic Kernel, LlamaIndex, DSPy, or equivalent technologies. Understanding of AI production monitoring includingmodel/application performance, latency, token consumption, cost, prompt and response tracing, model failures, retrieval quality, and system availability. Understanding ofAgentic AI concepts, including AI agents, planning and reasoning workflows, tool/function calling, agent orchestration, workflow automation, and multi‑agent systems. Understanding ofCI/CD, Infrastructure as Code, DevOps, MLOps, LLMOps, containerization, Kubernetes, and automated deployment practices. Understanding of data ingestion, ETL/ELT, data pipelines, data quality, metadata management, data governance, and data security concepts. Knowledge ofResponsible AI principles, AI governance, model risk management, data privacy, prompt security, content moderation, explainability, and human‑in‑the‑loop controls. Knowledge of emerging AI interoperability patterns and protocols such asModel Context Protocol (MCP)andAgent-to‑Agent (A2A)communication is desirable. Any experience with UCP is also a plus. Familiarity with observability platforms and technologies such asOpenTelemetry, CloudWatch, Azure Monitor, Google Cloud Operations, Datadog, Grafana, or equivalent tools. Strong stakeholder management skills with the ability to engage effectively withbusiness leaders, product owners, enterprise architects, engineering teams, security teams, and senior management. Excellent communication and presentation skills, with the ability to explain complex AI and architecture concepts to both technical and non‑technical audiences. Strong analytical and problem‑solving skills with the ability to evaluate multiple technical approaches and clearly articulate architectural trade‑offs. Ability to provide technical leadership and architectural guidance to cross‑functional engineering teams without necessarily being responsible for direct people management. Strong consulting mindset with the ability to understand business problems, challenge assumptions, identify technical risks, and recommend pragmatic solutions. Ability to work across multiple projects and technology domains while maintaining consistency with enterprise architecture principles and standards.

Additional Information

Relevant cloud architecture certifications such asAWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, Google Professional Cloud Architect, or equivalent certifications. AI/ML or Generative AI certifications from AWS, Microsoft, Google, Databricks, NVIDIA, or other recognized technology providers are advantageous. Experience working inlarge enterprise environments, consulting organizations, or complex multi‑cloud transformation programs. Experience defining or contributing to organizationalAI architecture standards, Generative AI platforms, AI Centers of Excellence, or enterprise AI governance frameworkswould be advantageous.

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