Solution Architect_GenAI (Agentic AI)

Solution Architect GenAI / Agentic AI

Experience: 10+ Years

Location: PAN India

Employment Type: Full-Time

Work Mode: Hybrid

Job Overview

We are looking for an experienced Solution Architect GenAI / Agentic AI with 10+ years of overall IT experience and strong expertise in solution architecture, Java, Spring Boot, Python, cloud-native technologies, and modern AI architectures.

The ideal candidate will have hands-on experience designing and implementing Generative AI and Agentic AI solutions, including LLM-based applications, RAG architectures, AI agents, prompt engineering, vector databases, and enterprise AI integrations.

The candidate should be capable of translating business requirements into scalable, secure, and production-ready technology architectures while providing technical leadership to engineering teams.

Key Responsibilities

• Define and design end-to-end solution architectures for enterprise applications and AI-powered platforms.
• Architect and implement GenAI and Agentic AI solutions using modern AI frameworks and technologies.
• Design LLM-powered applications incorporating RAG, prompt engineering, vector databases, AI agents, and enterprise data sources.
• Evaluate and select appropriate AI models, frameworks, platforms, and architectural approaches based on business requirements.
• Provide technical leadership across Java, Spring Boot, Python, cloud, microservices, and AI engineering.
• Design scalable and resilient cloud-native architectures on GCP, Azure, or AWS.
• Define microservices-based architectures and integrations using REST APIs and event-driven patterns.
• Guide development teams on architecture, design principles, coding standards, performance, scalability, and security.
• Design and oversee deployment architectures using Docker, Kubernetes, and CI/CD pipelines.
• Establish best practices for integrating AI capabilities into existing enterprise applications and platforms.
• Address AI-specific considerations including security, governance, privacy, explainability, responsible AI, and model risk.
• Conduct architecture reviews, technology evaluations, and proof-of-concepts for emerging AI technologies.
• Collaborate with business stakeholders, product managers, engineering teams, data teams, and security teams.
• Identify technical risks, define mitigation strategies, and ensure solutions meet enterprise architecture standards.

Mandatory Technical Skills

Solution Architecture

• 10+ years of overall IT experience with significant experience in Solution Architecture / Technical Architecture.
• Strong experience designing scalable, highly available, secure, and maintainable enterprise solutions.
• Ability to create architecture diagrams, technical designs, integration patterns, and technology roadmaps.
• Strong understanding of architecture principles, design patterns, scalability, performance, and resilience.

Java / Spring Boot / Python

• Strong hands-on expertise in Java.
• Strong experience with Spring Boot and developing enterprise-grade applications.
• Strong programming experience in Python, particularly for AI/ML and GenAI integrations.
• Experience integrating AI capabilities with existing Java/Python enterprise applications.

GenAI / Agentic AI

Hands-on experience with one or more modern GenAI / Agentic AI frameworks, such as:

• LangChain
• LangGraph
• CrewAI
• AutoGen
• Semantic Kernel

Strong understanding of:

• Generative AI architectures
• Agentic AI
• AI agents and multi-agent systems
• Tool/function calling
• Agent orchestration
• Memory and context management
• AI workflow orchestration
• LLM application architecture

LLM & RAG

Strong hands-on knowledge of:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Embeddings
• Vector Search
• Vector Databases
• Context management
• AI/LLM integrations
• Model/API integration
• LLM evaluation and optimization

Experience with vector databases such as Pinecone, FAISS, Chroma, Qdrant, Weaviate, Milvus, or equivalent is desirable.

Cloud & Cloud-Native Architecture

Strong experience with at least one major cloud platform:

• Google Cloud Platform (GCP)
• Microsoft Azure
• Amazon Web Services (AWS)

Good understanding of:

• Cloud-native application architecture
• Containers and container orchestration
• Serverless technologies
• Cloud security
• IAM
• Networking
• Scalability and high availability
• Monitoring and observability

Microservices & DevOps

Strong experience with:

• Microservices architecture
• REST APIs
• API Gateway
• Docker
• Kubernetes
• CI/CD
• Git-based development
• Automated build and deployment pipelines
• Application monitoring and logging

AI Governance & Security

The candidate should have a strong understanding of enterprise AI governance and responsible AI principles, including:

• AI security
• Data privacy and protection
• Responsible AI
• AI governance frameworks
• Model risk management
• Prompt injection and AI-specific security risks
• Data leakage prevention
• Access control
• Auditability and traceability
• Model monitoring and evaluation
• Ethical and responsible use of AI

Preferred / Good-to-Have Skills

• Experience with enterprise GenAI transformation initiatives.
• Experience building production-grade Agentic AI platforms.
• Exposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent AI platforms.
• Experience with LLMs such as GPT, Claude, Gemini, Llama, or equivalent.
• Knowledge of enterprise integration patterns and event-driven architecture.
• Experience with Kafka or other messaging platforms.
• Knowledge of SQL/NoSQL databases.
• Experience with observability and AI application monitoring.
• Experience leading architecture POCs and technology evaluations.

Key Competencies

• Solution Architecture
• Enterprise Architecture
• Java & Spring Boot
• Python
• GenAI / Agentic AI
• LLMs
• RAG
• Prompt Engineering
• Vector Databases
• LangChain / LangGraph / CrewAI / AutoGen / Semantic Kernel
• Cloud Architecture – AWS / Azure / GCP
• Microservices
• REST APIs
• Docker & Kubernetes
• CI/CD
• AI Security & Governance
• Responsible AI
• Technical Leadership
• Stakeholder Management

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.