– AI Solutions Architect

Job Title - AI Solutions Architect

Experience - 8–15 Years

Location - Hyderabad (Hybrid)

Role Overview

We are seeking an experienced AI Solutions Architect to lead the design, architecture, and delivery of enterprise-grade AI solutions. This is a customer-facing role that requires a blend of technical expertise, business consulting, and solution architecture skills.

The ideal candidate will work closely with clients to understand business challenges, define AI strategies, architect scalable Generative AI solutions, and lead engineering teams in delivering production-ready AI applications. You will be responsible for designing intelligent systems powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI workflows, and up-to-date AI platforms.

Key Responsibilities

Client Discovery &

- AI Consulting

- Engage with clients to understand business objectives, pain points, and digital transformation initiatives.

- Identify opportunities where AI can drive measurable business value.

- Translate business requirements into scalable AI solution architectures.

- Conduct AI discovery workshops, technical discussions, and solution presentations.

- Act as a trusted advisor throughout the solution lifecycle.

AI Solution Architecture

- Design enterprise-scale AI and Generative AI architectures.

- Architect Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge management and intelligent search.

- Design end-to-end RAG pipelines including document ingestion, preprocessing, embeddings, vector indexing, retrieval strategies, prompt orchestration, and response generation.

- Architect intelligent Agentic AI workflows capable of reasoning, planning, tool usage, and autonomous task execution.

- Define secure, scalable, and production-ready AI architectures aligned with enterprise standards.

Proof of Concept (POC) &

- Innovation

- Lead the development of AI Proof of Concepts (POCs) to validate business use cases.

- Build prototypes demonstrating AI capabilities and business outcomes.

- Evaluate emerging AI technologies, frameworks, and models to recommend the best-fit solutions.

Technical Leadership

- Provide architectural guidance to AI Engineers, Data Scientists, and Software Development teams.





- Review solution designs, code quality, scalability, security, and performance.

- Mentor engineering teams on AI best practices and modern architecture patterns.

- Define reusable AI components, design standards, and reference architectures.

Delivery &

- Implementation

- Collaborate with cross-functional teams to deliver AI solutions from concept to production.

- Oversee integration with enterprise applications, APIs, cloud platforms, and business workflows.

- Ensure solutions meet performance, governance, compliance, and security requirements.

Stakeholder Management

- Present architecture proposals, technical roadmaps, and implementation strategies to business and executive stakeholders.

- Support pre-sales activities, solution estimations, and technical proposal development.

- Partner with Product, Engineering, and Customer Success teams to ensure successful delivery and adoption.

Required Technical Skills

Generative AI

- Deep understanding of Large Language Models (LLMs)

- Prompt Engineering

- Function Calling

- Tool Calling

- AI Agents

- Agentic AI

- Model Context Protocol (MCP)

RAG Expertise

- Enterprise RAG Architecture

- Document Processing Pipelines

- Embedding Models

- Vector Search

- Hybrid Search

- Semantic Retrieval

- Context Management

- Prompt Orchestration

AI Frameworks

- LangChain

- LangGraph

- LlamaIndex

- CrewAI

- AutoGen

- Semantic Kernel

- OpenAI SDK

- Azure OpenAI

- Anthropic Claude

- Google Gemini APIs

Vector Databases

- Pinecone

- Weaviate

- Milvus

- ChromaDB

- FAISS

Programming

- Python

- REST APIs

- FastAPI

- SQL

- Git

Cloud Platforms

- Microsoft Azure

- AWS

- Google Cloud Platform

DevOps &

- Deployment

- Docker

- Kubernetes

- CI/CD Pipelines

- Azure DevOps / GitHub Actions

Required Experience

- 8–15 years of software engineering and solution architecture experience.





- 3 years of hands-on experience in Generative AI or AI solution architecture.

- Proven experience architecting enterprise AI applications using LLMs.

- Experience designing and implementing RAG applications and RAG pipelines.

- Experience building Agentic AI workflows and multi-agent systems.

- Strong client-facing consulting experience with the ability to translate business problems into AI solutions.

- Experience leading cross-functional technical teams and driving end-to-end solution delivery.

- Experience developing AI Proofs of Concept (POCs) and scaling them into production solutions.

Preferred Qualifications

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

- AI or Cloud certifications (Azure AI Engineer, AWS Machine Learning, Google Professional Machine Learning Engineer, etc.) are an advantage.

- Experience in enterprise digital transformation or consulting engagements.

Key Competencies

- AI Solution Architecture

- Enterprise AI Strategy

- Client Consulting &
- Stakeholder Management

- Solution Design &

- Technical Leadership

- Systems Thinking

- Problem Solving

- Innovation &

- Continuous Learning

- Communication &

- Presentation Skills

- Cross-functional Collaboration

Success Measures

- Design scalable, secure, and production-ready AI solutions aligned with client objectives.

- Deliver successful AI implementations with measurable business outcomes.

- Build robust RAG applications, intelligent Agentic workflows, and enterprise AI platforms.

- Lead high-performing technical teams and ensure architectural excellence.

- Drive AI innovation while maintaining governance, security, and operational efficiency.

1. Hands-on experience in Generative AI or AI solution architecture

2. Production experience with GenAI applications (not just prototypes)

3. Experience designing and implementing RAG applications and RAG pipelines

4. Strong customer-facing consulting and solution architecture experience

5. Experience developing AI Proofs of Concept (POCs) and scaling them into production solutions.

6. Leadership in guiding engineering teams from design through deployment

📌 Solution Architect (Hyderabad)
🏢 Athena Global Technologies
📍 Hyderabad