AI Architect – GenAI & ML

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-premises 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-premises 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

• Bachelor’s degree in computer science, Artificial Intelligence, Data Science, or a related field; Master’s degree preferred
• 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-premises constraints.

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