AI Engineer Lead

We are seeking an experienced AI Engineer Lead to lead the engineering and production implementation of enterprise AI and Generative AI solutions on Microsoft Azure. The role requires strong hands-on expertise in Generative AI, LLMs, RAG, Agentic AI, Microsoft Foundry, Azure OpenAI, Azure AI Search, Python, APIs, and cloud-native application development

The AI Engineer Lead works closely with AI Architects, application teams, data engineers, cloud/platform teams, and security teams to turn architecture into production-ready, scalable, and maintainable solutions, while providing technical leadership to the engineering team and engaging directly with the business.

Key Responsibilities:

Engineering Delivery & Leadership

Own engineering delivery, including sprint planning, estimation, technical risk and dependency management, and release management.

Lead and mentor AI engineers and developers; provide technical direction and resolve complex engineering challenges.

Own code quality: conduct code and design reviews and enforce engineering standards and engineering governance

Own production readiness and take technical ownership of solutions through enterprise production deployment.

Break architecture/design into implementable technical components and engineering stories.

Lead technical PoCs and convert successful prototypes into production solutions.

Hands-On Build: Lead hands-on development of enterprise AI and Generative AI applications using Microsoft Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related Azure AI services

Implement RAG solutions: data ingestion and preprocessing, document chunking, embedding generation, vector and hybrid search, retrieval and reranking, context construction, and LLM grounding and response generation.

Build AI agents and agentic workflows that interact with enterprise applications, APIs, data, and knowledge sources

Develop reusable AI components, frameworks, APIs, accelerators, and engineering patterns

Implement prompt engineering, prompt/version management, structured outputs, function/tool calling, and multimodal AI use cases.

Integrate AI capabilities into existing enterprise applications and workflows

Stakeholder Engagement: Work with Product Owners and business teams to translate requirements into technical solutions.

Collaborate with AI Architects to implement approved solution designs and raise design feedback from the engineering perspective.

Experience & Qualifications:

8+ years of software engineering / application development experience.

3+ years working with cloud-native / Azure technologies.

Strong hands-on experience with AI/ML or Generative AI solutions; demonstrated LLM/RAG application delivery.

Experience leading AI engineering teams through design, development, testing, and deployment.

Experience managing delivery of AI programs and taking solutions from PoC through enterprise production deployment.

Strong experience troubleshooting complex application/integration issues

Experience working directly with architects, customers, and business stakeholders.

Must-Have Skills

Large Language Models and Generative AI

RAG, Agentic AI, Prompt engineering

Embeddings, vector search, and hybrid search, Function/tool calling and structured output

LLM evaluation

Grounding techniques and hallucination mitigation

Guardrails / content safety, Token and context management, Model selection and benchmarking

Python, APIs, and Azure