Artificial Intelligence Architect/Tech Lead

Description :

We are building the AI backbone powering our Microsoft 365-native product ecosystem and are looking for a hands-on AI Architect / Tech Lead to help scale our AI platform and engineering capabilities.

This role is focused on building production-grade AI systems, scalable AI infrastructure, and multi-tenant AI architectures not research or consulting. The ideal candidate should have strong experience designing and deploying AI-powered applications, RAG pipelines, AI agents, and secure enterprise AI systems on Azure.

Key Responsibilities :

- Design and build a shared AI platform and reusable AI service layer across products.

- Architect and implement secure multi-tenant RAG pipelines on Microsoft 365 data.

- Build and scale AI agents from prototype to supervised production environments.

- Develop scalable LLM orchestration frameworks, evaluation systems, observability, and monitoring layers.

- Define AI engineering standards, governance frameworks, and platform best practices.

- Work closely with engineering teams to convert architecture into execution-ready technical designs and implementation plans.

- Design secure integrations using Microsoft Graph API, Azure OpenAI, and enterprise SaaS architectures.

- Optimize AI system performance, scalability, reliability, and cost efficiency.

- Collaborate with product, engineering, and leadership teams to drive AI platform adoption and roadmap execution.

Required Skills & Experience :

- 4 to 9 years of experience in software engineering, AI engineering, or platform engineering.

- Strong hands-on experience with :

1. RAG (Retrieval-Augmented Generation)

2. Azure OpenAI

3. Microsoft Graph API

4. LLM-based applications

5. AI agents / Agentic AI systems

- Strong expertise in multi-tenant SaaS security architecture.

- Experience building and deploying production-grade AI systems at scale.

- Strong understanding of :

1. LLM orchestration

2. Prompt engineering

3. Vector databases

4. AI observability and evaluation frameworks

5. AI governance and security

- Experience with cloud-native development on Azure.

- Strong backend engineering and API architecture knowledge.

- Excellent problem-solving and system design skills.

- Ability to mentor developers and drive engineering standards.

Good to Have :

- Experience with LangChain, Semantic Kernel, AutoGen, CrewAI, or similar frameworks.

- Exposure to enterprise AI governance and compliance models.

- Experience with Kubernetes, Docker, CI/CD pipelines, and scalable cloud deployments.

- Familiarity with Microsoft 365 ecosystem integrations.

Qualifications :

- Bachelors or Masters degree in Computer Science, Engineering, or related field.

- Equivalent practical experience in AI platform engineering will also be considered.