AI Application Engineer / Architect

Key Responsibilities: Architect, design, and implement scalable AI applications leveraging multi-agent frameworks and agentic workflows. Develop and optimize complex stateful agent orchestration solutions using Lang Graph and related Lang Chain technologies. Design and implement advanced Retrieval-Augmented Generation (RAG) solutions utilizing Azure AI Search for high-quality knowledge retrieval and contextual reasoning. Build intelligent AI systems capable of autonomous decision-making, task planning, memory management, and multi-step execution. Integrate large language models from Azure OpenAI and other AI platforms into enterprise-grade applications. Partner with business stakeholders to gather requirements, define AI use cases, and translate business needs into technical solutions. Drive architecture decisions, governance standards, and best practices for AI application development. Optimize AI application performance, scalability, observability, and security. Collaborate with software engineers, data engineers, and Dev Ops teams to deliver production-ready AI solutions. Evaluate emerging AI technologies, frameworks, and methodologies to continuously improve platform capabilities. Establish monitoring, testing, and evaluation frameworks for AI applications to ensure quality, reliability, and responsible AI practices.

Required Qualifications: Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field.5+ years of software engineering experience with at least 2+ years focused on AI/ML application development. Hands-on experience designing and deploying enterprise AI applications. Strong expertise with Lang Chain, Lang Graph, and agent-based AI architectures. Experience implementing RAG solutions using Azure AI Search, vector databases, and semantic search technologies. Proficiency with Python and modern software development practices. Strong understanding of large language models (LLMs), prompt engineering, embeddings, vector search, memory systems, and tool integration. Experience developing cloud-native applications within Microsoft Azure. Knowledge of API design, microservices architecture, containerization, and CI/CD pipelines. Excellent problem-solving, communication, and stakeholder management skills.

Preferred Qualifications: Experience with Azure OpenAI Service, Cognitive Services, and other Azure AI offerings. Familiarity with vector databases such as Pinecone, Weaviate, Chroma, or Azure-native vector storage solutions. Experience implementing multi-agent systems, autonomous agents, and workflow orchestration platforms. Knowledge of MLOps, AI observability, and model evaluation frameworks. Experience with enterprise security, compliance, and responsible AI governance. Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or related disciplines.