Technical Architect-Azure AI

Job Description

Key Responsibilities

Pre-Sales & Client Engagement

- Act as the AI subject matter expert in client conversations, workshops, and solution envisioning sessions.
- Support proposal development, architecture documentation, and effort estimation for AI projects.
- Translate complex business needs into high-level and detailed technical architecture diagrams and blueprints.

Design Azure AI Solutions

- Define architectural patterns for integrating LLMs, generative AI, RAG pipelines, vector databases (e.g., Azure AI Search, FAISS), and orchestration frameworks (e.g., Semantic Kernel, LangChain).
- Support team in building Azure Cost estimates for various types of solution archetypes, provide guidance on selecting the most cost-effective Azure AI service configurations (e.g., model size, region, compute SKUs).
- Ensure scalability, security, governance, and compliance are foundational in all designs.

Copilot Extension & Customization

- Provide subject matter expertise in using Azure AI Studio to extend and fine-tune Microsoft 365 Copilots and custom business Copilots.
- Integrate enterprise data and logic to create domain-specific copilots aligned with customer workflows.
- Apply prompt engineering and grounding strategies to enhance context awareness and action-taking capabilities.

Advanced AI Pipeline Development

- Build retrieval-augmented generation (RAG) pipelines and advanced RAG pipelines (such as GraphRAG) using Azure AI Search, Cognitive Search, and Semantic Kernel.
- Design chunking, embedding, ranking, and post-processing strategies to optimize search relevance and model responses.
- Connect unstructured and structured content (e.g., PDFs, Word, Excel, D365 data) to GenAI models for personalized search and Q&A.
- Leverage Azure Custom Skills to enrich data across the pipeline to build enterprise-grade scaled AI solutions

AI Agent Design and Orchestration

- Design and deploy multi-step AI agents that reason, retrieve, and take action based on dynamic workflows.
- Develop solution archetypes (e.g., knowledge assistants, workflow copilots, policy advisors) powered by Semantic Kernel or Azure OpenAI Function Calling.
- Implement agent memory, state management, and tool integration (APIs, plugins, functions).

AI Lifecycle Engineering

- Automate deployment using Azure DevOps, CI/CD, and containerization where needed.
- Monitor model outputs for relevance, accuracy, and alignment to responsible AI principles.
- Collaborate with data engineers, solution architects, and UX designers for full-stack deployment.

Technical Leadership & Delivery Oversight

- Provide technical leadership on AI implementations, including model training, inferencing, and MLOps integration.
- Conduct architecture reviews, code walkthroughs, and design governance.

Collaboration and Communication

- Collaborate cross-functionally (software engineering, product, design, etc.) to align AI solutions with business goals.
- Document and communicate technical designs, roadmaps, and updates to both technical and non-technical stakeholders.

Research and Innovation

- Stay current on emerging AI/ML and Generative AI trends, frameworks, and tools, recommending advancements that can enhance our AI initiatives.
- Champion a culture of continuous learning and experimentation across the organization.

Qualifications

- Education:
- Bachelor s or Master s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent experience).
- Experience:
- 5+ years of hands-on AI/ML engineering experience, with a strong focus on Microsoft Azure.
- Proven experience with Generative AI models (LLMs, text or image generation) is highly desirable.
- Required Skills:
- 5+ years of experience in AI/ML solution engineering, with a focus on Microsoft Azure AI services.
- Proficient with:
- Azure AI Studio, Azure OpenAI Service, Azure Cognitive Search / AI Search
- Semantic Kernel, LangChain (preferred), or similar orchestration frameworks
- Prompt engineering, grounding techniques, embedding models
- Experience building RAG-based applications and deploying GenAI models in production.
- Strong Python and/or C# skills for SDK use and orchestration logic.
- Familiarity with REST APIs, Azure Functions, and plugin development.
- Azure certifications (e.g., Azure AI Engineer Associate, Solutions Architect).
- Experience with Copilot Studio, Graph RAG, or Microsoft Fabric integrations.
- Knowledge of Responsible AI, prompt safety, and guardrail implementation.

Nice to have skills:

Soft Skills:

- Excellent problem-solving and analytical abilities.
- Strong communication and collaboration skills.
- Ability to work in a fast-paced, agile environment, managing multiple priorities effectively.

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