AI Architect (GenAI / Agentic AI)

Austin, TX (Onsite)

Role Summary

We are seeking an experienced AI Architect to design and lead end-to-end implementation of enterprise-grade Generative AI and Agentic AI solutions. The ideal candidate will have strong experience in LLMs, RAG pipelines, multi-agent systems, and scalable backend engineering, with proven capability in architecting production-ready AI platforms.

Experience Required

·                10+ years of overall software development experience

·                3+ years in architecture / solution design roles

• Proven experience delivering enterprise-scale AI/GenAI solutions

Key Responsibilities

Architecture & Design

·                Define and implement end-to-end AI/GenAI architecture covering:

o        LLM integration

• RAG pipelines
• Multi-agent orchestration frameworks
• Design scalable, secure, and cost-efficient AI platforms and services
• Evaluate and select appropriate LLMs, frameworks, and toolchains

GenAI & LLM Solutions

·                Design and develop GenAI-powered applications using Python and modern frameworks

• Implement:

o        Prompt engineering strategies

o        Token optimization and token economics

o        Response grounding and evaluation frameworks

• Integrate enterprise data sources into RAG pipelines and knowledge systems

Agentic AI / Multi-Agent Systems

·                Lead design and implementation of agent-based solutions, including:

o        Agent design & orchestration

• Agent development and lifecycle management
• Agent hardening, testing, and evaluation
• Governance and monitoring frameworks
• Build multi-agent ecosystems with role-based and task-based agents

Platform & Integration

·                Develop and expose REST APIs and microservices for AI consumption

• Implement:

o        Tool calling frameworks

o        Model Context Protocol (MCP) integrations

o        MCP server creation and management

• Integrate with enterprise platforms, workflows, and external APIs

Engineering Excellence

·                Ensure best practices in:

o        Code quality, scalability, and performance

• CI/CD for AI systems (LLMOps / MLOps)
• Security, governance, and compliance
• Collaborate with cross-functional teams (Data, Engineering, Product)

Mandatory Skills

Programming & Core Engineering

·                Strong expertise in Python

• Experience building REST APIs and scalable backend systems

Generative AI & LLMs

·                Hands-on experience with:

o        LLM integration (OpenAI, Azure OpenAI, etc.)

o        Prompt engineering and optimisation

o        GenAI-powered application development

RAG & Knowledge Systems

·                Expertise in:

o        RAG pipeline design & implementation

• Vector databases and semantic search
• Data chunking, indexing, and retrieval strategies

Agentic AI

·                Deep experience in:

o        Multi-agent systems architecture

• Agent orchestration frameworks (e.g., LangGraph, CrewAI, similar)
• Agent lifecycle: design → build → evaluate → govern

Advanced AI Concepts

·                Tool calling and tool orchestration

• Token usage optimisation and cost strategies
• Evaluation frameworks for GenAI outputs

MCP & Advanced Integrations

·                Experience with:

o        MCP integration

o        MCP server setup and management

• Context-aware AI system design

Good to Have

·                Experience with LLMOps / MLOps practices

• Exposure to cloud AI platforms (Azure, AWS Bedrock, Google Cloud Platform Vertex AI)
• Knowledge of Graph RAG / hybrid retrieval systems
• Familiarity with enterprise security, governance, and compliance frameworks