| Job Description: |
Role Summary:
The Generative AI Lead Engineer will lead the design, development, and adoption of enterprise-scale AI solutions. This role blends hands-on engineering, solution architecture, and technical leadership to deliver secure, scalable, and business-aligned Generative AI platforms using Azure OpenAI and cloud-native technologies. The ideal candidate has strong experience delivering end-to-end AI-powered applications, from data ingestion and model integration to APIs and lightweight user interfaces for internal tools, PoCs, and business-facing solutions. Deep frontend specialization is not required, but the ability to independently deliver complete AI solutions is essential.
Key Responsibilities
Lead the design and implementation of Generative AI solutions using Azure OpenAI (GPT-4o, GPT-5, embeddings, RAG architectures).
Build and validate Proofs of Concept (PoCs) to assess feasibility, scalability, performance, and business value.
Design and deploy RAG-based AI systems, including vector search, prompt orchestration, and knowledge grounding.
Deliver end-to-end AI applications, including backend services, APIs, and lightweight UI components for AI-driven workflows.
Actively leverage AI agents and agentic frameworks for tasks such as code generation, testing, refactoring, workflow automation, and intelligent orchestration.
Develop and integrate custom copilots using Copilot Studio, Azure AI Foundry, Entra ID, Microsoft Graph API, and enterprise applications.
Architect and build cloud-native applications using Azure Functions, App Services, and microservices on Azure Contianer Registry/AKS.
Design data ingestion pipelines from SharePoint, Cosmos DB, and external APIs for AI-driven solutions.
Ensure AI solutions meet enterprise security, compliance, and governance standards.
Partner with product owners, architects, security teams, and business stakeholders to drive AI adoption.
Provide technical leadership through design reviews, mentoring, and best practice guidance.
Contribute to AI standards, reference architectures, and operational runbooks.
Must-Have Requirements
8+ years of experience in software, platform, or cloud engineering.
3+ years of hands-on experience with AI/ML or Generative AI solutions.
Strong experience with Azure OpenAI and LLM-based application development.
Proven experience implementing RAG architectures and vector search solutions.
Experience delivering end-to-end AI applications, including backend services, APIs, and basic user interfaces for AI-driven solutions.
Experience using AI agents or agentic workflows to assist with software development, automation, or intelligent orchestration.
Solid background in Azure cloud services (Functions, App Services, ACR).
Familiarity with AI observability, including model monitoring, logging, and usage analytics.
Hands-on experience with Azure AI Search, Azure Document Intelligence, or similar services.
Strong programming experience in Python and or NodeJS/TypeScript.
Experience with prompt engineering, evaluation strategies, and LLM performance tuning.
Experience designing scalable, resilient, and secure enterprise systems.
Excellent communication skills with the ability to translate business needs into technical solutions.
Preferred Skills:
Experience implementing authentication and authorization using Entra ID, managed identities, and RBAC.
Infrastructure as Code experience using Terraform or Bicep.
CI/CD experience using GitHub Actions.
Understanding of data privacy, PII handling, and responsible AI practices.
Experience working in regulated or large enterprise environments.
Prior experience acting as a technical lead.
Exposure to Azure Document Intelligence, Power Platform, or Copilot Studio. |
