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 - AI with 10+ years of overall technology experience, including at least 3 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 as AWS, 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 including Generative 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-end AI and Generative AI solution architectures that address business requirements while meeting enterprise standards for scalability, security, performance, reliability, and maintainability. 

• Define appropriate architecture patterns for Generative AI, Agentic AI, RAG, Machine Learning, intelligent automation, conversational AI, and AI-enabled enterprise applications. 

• Provide architectural leadership across the complete solution lifecycle, from discovery, 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 the architecture and solution design of 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 secure solution architecture designs, architecture diagrams, integration patterns, and technical specifications. 
• Design AI solutions using services and technologies available on AWS, Microsoft Azure, or Google Cloud Platform, including managed AI/ML, data, integration, compute, security, and observability services. 
• Define architectures for LLM-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 with APIs, databases, data platforms, SaaS applications, enterprise applications, event-driven systems, and external services. 
• Evaluate and recommend appropriate foundation models, LLMs, embedding models, AI platforms, vector databases, orchestration frameworks, and supporting technologies based on business, technical, security, cost, and performance requirements. 
• Define architecture approaches for AI security, identity and access management, data privacy, guardrails, content filtering, responsible AI, model governance, and regulatory compliance. 
• Establish architectural standards for AI 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 conduct AI use-case discovery, technical feasibility assessments, architecture assessments, technology evaluations, and solution option analysis. 
• Ability to create and communicate high-level architecture, detailed solution architecture, sequence diagrams, data flows, integration diagrams, deployment architectures, and architecture decision records. 
• Ability to design appropriate AI guardrails addressing areas such as hallucination, prompt injection, data leakage, harmful content, unauthorized tool execution, and inappropriate model responses. 
• Ability to define reference architectures, reusable architecture patterns, technical standards, and architecture governance frameworks for AI solutions. 

Professional and Technical Skills

• 10+ years of overall experience in software engineering, application architecture, cloud architecture, data architecture, solution architecture, or related technology roles. 
• Minimum 3 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 a Solution Architect, Technical Architect, Cloud Architect, AI Architect, or equivalent senior architecture role. 
• Proven experience designing and delivering enterprise-scale, production-grade solutions involving multiple applications, platforms, integrations, and data sources. 
• Hands-on architecture experience with Retrieval-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 of cloud-native architecture patterns, including microservices, APIs, serverless architectures, containers, event-driven architectures, asynchronous processing, and distributed systems. 
• Strong understanding of REST APIs, API gateways, authentication and authorization, OAuth/OIDC, networking, secrets management, encryption, and enterprise security architecture. 
• Strong understanding of enterprise data architectures including data lakes, data warehouses, lakehouses, relational databases, NoSQL databases, vector databases, and streaming platforms. 
• Strong understanding of enterprise security architecture including Identity 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 across cost, performance, scalability, security, maintainability, complexity, and time to market. 
• Experience integrating AI applications with enterprise data platforms such as Databricks, Snowflake, BigQuery, Amazon Redshift, Microsoft Fabric, or equivalent technologies. 
• Experience designing integrations between AI solutions and enterprise systems using APIs, 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 including discovery, 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 as LangChain, LangGraph, Semantic Kernel, LlamaIndex, DSPy, or equivalent technologies. 
• Understanding of AI production monitoring including model/application performance, latency, token consumption, cost, prompt and response tracing, model failures, retrieval quality, and system availability. 
• Understanding of Agentic AI concepts, including AI agents, planning and reasoning workflows, tool/function calling, agent orchestration, workflow automation, and multi-agent systems. 
• Understanding of CI/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 of Responsible 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 as Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication is desirable. Any experience with UCP is also a plus. 
• Familiarity with observability platforms and technologies such as OpenTelemetry, CloudWatch, Azure Monitor, Google Cloud Operations, Datadog, Grafana, or equivalent tools. 
• Strong stakeholder management skills with the ability to engage effectively with business 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 as AWS 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 in large enterprise environments, consulting organizations, or complex multi-cloud transformation programs. 
• Experience defining or contributing to organizational AI architecture standards, Generative AI platforms, AI Centers of Excellence, or enterprise AI governance frameworks would be advantageous. 

 

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