AI Architect – GenAI & ML

About the Role

We are looking for a hands‑on AI Architect with strong expertise in Generative AI and Machine Learning to lead the design and delivery of production grade AI solutions. This role involves end‑to‑end ownership across multiple AI initiatives, combining deep technical execution with solution architecture, stakeholder engagement, and a strong focus on quality, evaluation, and responsible AI.

Key Responsibilities

• Own end‑to‑end delivery of multiple AI and GenAI projects from requirement understanding to production deployment
• Lead solution architecture and design decisions across GenAI and ML use cases, ensuring scalability, security, and performance
• Engage with business and client stakeholders to translate requirements into practical AI solutions
• Remain hands‑on with implementation, including building APIs, integrating AI services, and troubleshooting production issues
• Manage multiple AI initiatives in parallel, ensuring timelines, quality, and stakeholder alignment
• Troubleshoot and optimize production AI systems including performance, latency, and model quality issues
• Drive architectural trade‑offs across cost, latency, accuracy, and infrastructure constraints including cloud and on‑premise options
• Design and implement RAG pipelines using Azure AI Search with vector, semantic, and hybrid retrieval strategies
• Build LLM powered applications using Azure OpenAI with strong prompt engineering and structured output techniques
• Develop AI driven solutions for summarization, classification, document intelligence, Q&A, and automation use cases
• Implement human in the loop workflows and red teaming approaches to continuously improve model outputs
• Own AI evaluation frameworks including benchmarking, testing, and performance validation using tools such as RAGAS, BLEU, and precision and recall metrics
• Implement observability and tracing using tools such as Langfuse, LangSmith, or Azure AI Foundry evaluations
• Design and enforce guardrails including content filtering, prompt injection protection, bias mitigation, and compliance controls
• Deploy solutions on Azure with CI/CD pipelines, logging, monitoring via Application Insights, and full audit trails
• Design hybrid architectures considering data residency, on‑premise GPU constraints, and regulatory requirements
• Develop and evaluate traditional ML models for classification, clustering, prediction, and other use cases
• Mentor junior engineers and ensure overall solution quality across the team

Requirements

• 8 or more years of experience in AI/ML engineering and solution architecture
• 3 or more years of hands‑on experience in GenAI and LLM based applications in production
• Proven experience leading multiple AI projects in enterprise environments with direct stakeholder engagement
• Strong understanding of RAG architectures, embeddings, vector databases, and LLM orchestration frameworks
• Experience with agentic AI frameworks such as LangGraph, AutoGen, Semantic Kernel Agents, or CrewAI for building multi‑step reasoning and orchestration pipelines
• Hands‑on experience with LLM observability and tracing tools such as Langfuse, LangSmith, or Azure AI Foundry
• Experience with AI evaluation frameworks including RAGAS and standard ML metrics such as BLEU, precision, recall, and F1
• Experience designing guardrail systems using tools such as Guardrails AI, NeMo Guardrails, or Azure content safety
• NLP experience including classification, named entity recognition, embeddings, and fine tuning with LoRA or PEFT
• Strong understanding of responsible AI, bias mitigation, red teaming, and auditability requirements
• Strong Python skills including FastAPI, Pandas, and NumPy with experience building and deploying production APIs
• Hands‑on experience with the Azure AI stack including Azure OpenAI, AI Search, AI Foundry, Document Intelligence, AI Language, AI Vision, AI Speech, Cosmos DB, and Application Insights
• Experience with HuggingFace Transformers for open source model development and evaluation
• Experience working in regulated environments such as banking, fintech, or government is preferred
• Experience designing solutions under data residency, compliance, and hybrid cloud and on‑premise constraints
• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field; Master’s degree preferred

Good to Have

• Open source LLM deployment and evaluation on GPU infrastructure including LLaMA, Mistral, Qwen, or DeepSeek
• Multi‑cloud exposure with AWS Bedrock or GCP Vertex AI
• NVIDIA AI Enterprise stack including Triton Inference Server or NIM microservices
• Multilingual NLP experience particularly Arabic language models
• Microsoft Azure certifications such as AI-102, DP‑100, or AZ‑305