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.