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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Azure OpenAI Engineer - **Company:** OpenKyber LLC - **Location:** Santa Clara, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, C++ (Programming Language), Cloud Computing, Encodings, Data Governance, Decision Support Systems, Memory Management, Elasticsearch, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Knowledge Management, PostgreSQL, Machine Learning, Role-Based Access Control, Tensorflow, Azure Data Lake, Search Technologies, Software Deployment, Software Engineering, SQL Databases, TypeScript, Management of Software Versions, Web Services, Google Cloud, Data Classification, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Backend, Fastapi, Build Management, AI Platforms, Kubernetes, HuggingFace, Azure AKS, Data Management, Machine Learning Operations, Virtual Agents, Restful APIs, Grpc, GPT, Serverless Computing, Databricks, Programming Languages - **Published:** August 15, 2026 - **Apply:** https://www.adzuna.com/details/5845814302 ## About the Role Category Required / Preferred Stack Programming Languages - Mandatory Python; SQL Programming Languages - Preferred TypeScript; JavaScript; C++ AI Frameworks PyTorch; Hugging Face Transformers; TensorFlow; MLflow Agent Frameworks LangGraph; LangChain; Semantic Kernel; AutoGen Vector Databases Azure AI Search; Elasticsearch/OpenSearch; Chroma; PGVector Backend FastAPI; REST APIs; gRPC (preferred) Data Platforms Databricks; Fabric; PostgreSQL 5. Cloud Environment Primary Microsoft Azure / AWS Services Azure AI Foundry Azure OpenAI Azure AI Search Azure Functions Azure Kubernetes Service (AKS) ADLS Gen2 Preferred Additional Experience AWS Google Cloud Platform 6. Security, Compliance & Data Classification Mandatory Understanding of enterprise security controls. Experience handling Internal and Confidential data. Secure API design. RBAC and identity management. Preferred Responsible AI implementation. Data governance frameworks. Model monitoring and auditability. PII protection and redaction. AI risk assessment and guardrails. 7. Expected Deliverables & Success Criteria First 6 Months 1 2 production AI applications. Enterprise RAG framework. Agent orchestration framework. Evaluation and observability dashboards. First 12 Months Multiple production deployments. Reusable AI platform components. Reduced deployment time and development effort. Adoption across multiple teams. Success Metrics User adoption. Productivity impact. Response quality. Hallucination reduction. Platform reusability. Deployment velocity. 10. Required Years of Experience Mandatory 5 8 years Software Engineering 3+ years AI/ML Engineering 1 2 years Generative AI Preferred 2+ years building production GenAI systems. Experience leading technical workstreams. 11. Mandatory vs Preferred Skills Mandatory Python LLM application development RAG architecture design PyTorch or TensorFlow REST APIs Azure cloud Vector databases AI evaluation techniques Preferred Multi-agent systems Scientific AI Model fine-tuning Multimodal AI MLOps Databricks Kubernetes MCP ecosystem Certifications (Preferred) Azure AI Engineer Associate Azure Solutions Architect Databricks ML Professional AWS ML Specialty 12. Prior Experience with Agentic AI, LLMs & Production Deployments Mandatory Experience - LLMs GPT-family models Claude Llama Mistral Gemini RAG Chunking strategies Embedding generation Hybrid retrieval Reranking Evaluation methodologies Agentic AI Tool calling Function calling Workflow automation Memory management Planning and execution frameworks AI Orchestration Frameworks - Experience with at least one LangGraph Semantic Kernel AutoGen CrewAI Production Deployment CI/CD for AI applications Monitoring and observability Prompt versioning Model lifecycle management Cost optimization ## Description Job description: AI Application Engineer / Lead Role Requirements & Hiring Criteria Location Experience Priority Santa Clara, CA (Onsite) 5 8 yrs SWE; 3+ yrs AI/ML; 1 2 yrs GenAI Production AI / Agentic AI 1. Business Objectives & Expected Outcomes Business Objectives Build enterprise-grade AI applications that improve engineering, R&D, manufacturing, and knowledge management workflows. Accelerate adoption of Agentic AI across Applied Materials. Establish reusable AI platform components and frameworks. Reduce development effort through AI-assisted workflows and reusable services. Expected Outcomes Deploy production AI applications used by multiple business units. Deliver measurable productivity improvements. Create reusable RAG, agent, and orchestration frameworks. Improve knowledge discovery and decision support across engineering teams. 2. Detailed Job Description & Key Responsibilities AI Application Development Design and build AI-powered applications using LLMs and foundation models. Develop RAG solutions leveraging enterprise knowledge sources. Build multi-agent systems for complex workflows. Agentic AI Design planning, reasoning, tool-calling, and workflow orchestration systems. Build autonomous and human-in-the-loop agent architectures. Develop domain-specific AI copilots. AI Engineering Fine-tune, evaluate, and optimize models. Implement prompt engineering and evaluation frameworks. Build API services for AI model consumption. Leadership Lead technical solution design. Mentor junior engineers. Driving AI engineering best practices. Partner with R&D, product, and business stakeholders. 4. Technical Stack, Frameworks & Programming Languages ## Related Videos - [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Exploring the Power of gRPC-Gateway for Writing RESTful Services](https://www.wearedevelopers.com/videos/2072-exploring-the-power-of-grpc-gateway-for-writing-restful-services) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Building and Deploying Multi-Agent Systems with ADK and Vertex AI](https://www.wearedevelopers.com/videos/1918-building-and-deploying-multi-agent-systems-with-adk-and-vertex-ai) - [Boosting OpenSearch Performance: gRPC Search in Action](https://www.wearedevelopers.com/videos/1964-boosting-opensearch-performance-grpc-search-in-action) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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