Solution Architect - AI

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.

Work arrangement
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