AI Architect (GenAI & Agentic AI)

About the Role

We are seeking an experienced AI Architect to lead the design and delivery of enterprise-grade AI solutions. This role is ideal for someone who combines deep expertise in Generative AI, cloud-native architectures, and enterprise software engineering with the ability to translate business challenges into scalable AI platforms.

You will work closely with business stakeholders, enterprise architects, and engineering teams to define AI strategy, architect intelligent systems, and drive the successful implementation of AI-powered applications across the enterprise.

Key Responsibilities

Lead the architecture and design of enterprise AI and Generative AI solutions.

Design scalable AI platforms leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and modern cloud services.

Develop AI reference architectures, reusable frameworks, and engineering best practices.

Partner with business and technology stakeholders to identify AI use cases and define implementation roadmaps.

Guide engineering teams through architecture reviews, technical decision-making, and solution delivery.

Design secure, scalable integrations with enterprise applications, APIs, and data platforms.

Establish AI governance, observability, security, and responsible AI practices.

Mentor engineering teams and drive technical excellence across AI initiatives.

Stay current with emerging AI technologies and evaluate their applicability to enterprise use cases.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

10+ years of software engineering, solution architecture, or enterprise architecture experience.

3+ years designing and delivering Generative AI or Machine Learning solutions in production.

Strong experience designing cloud-native architectures on Azure, AWS, or GCP.

Hands-on experience with Large Language Models (OpenAI, Azure OpenAI, Anthropic, Gemini, etc.).

Experience implementing Retrieval-Augmented Generation (RAG), vector databases, semantic search, and prompt engineering.

Strong programming experience in Python and modern API development frameworks.

Experience with containerization, Kubernetes, CI/CD, Infrastructure as Code, and modern DevOps practices.

Strong understanding of distributed systems, APIs, microservices, and enterprise integration patterns.

Preferred Qualifications

Experience with AI Agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.

Experience building enterprise knowledge platforms and AI-enabled search solutions.

Exposure to MLOps, LLMOps, AI observability, and model lifecycle management.

Experience integrating AI solutions with enterprise platforms such as ServiceNow, GitHub, Jira, cloud infrastructure, or ITSM tools.

Experience working within highly regulated industries such as Financial Services, Insurance, Healthcare, or Telecommunications.

Technical Skills

Python

Azure OpenAI / OpenAI APIs

LangChain, LangGraph, CrewAI or equivalent frameworks

RAG, Vector Databases (Azure AI Search, Pinecone, pgVector, Weaviate, etc.)

FastAPI / REST APIs

Azure, AWS, or GCP

Docker & Kubernetes

GitHub Actions / Azure DevOps

SQL & NoSQL Databases

Terraform or Infrastructure as Code

Leadership Competencies

Strong consulting and stakeholder management skills.

Ability to influence technical and business leadership.

Excellent communication and presentation skills.

Ability to lead cross-functional engineering teams in agile environments.

Passion for innovation, continuous learning, and solving complex business problems through AI.