Full-Stack AI Engineer

As a Full-Stack AI Engineer at Cyderes, you will play a crucial role in developing production-grade agentic AI systems. Your focus will be on prompt engineering, Python backend services, Azure-native cloud architecture, and full-stack delivery of reliable AI workflows at scale. You will report to the Engineering Manager for seamless coordination. Responsibilities: - Prompt Engineering & Agentic Systems - Design, test, and enhance enterprise-grade prompts, system instructions, schemas, and agent workflows. - Build multi-step agentic pipelines, self-refining loops, and deterministic fallback logic. - Implement PromptOps practices such as versioning, evaluations, regression testing, token optimization, and governance. - Develop RAG pipelines, vector search, memory layers, and grounding strategies. - Backend & Full-Stack Engineering - Create Python-based microservices using FastAPI, async patterns, and REST APIs. - Deliver full-stack AI applications (React, TypeScript) for multi-turn AI interactions. - Manage GitHub, CI/CD, testing, branching, and release automation. - Integrate AI services through serverless, containerized, and event-driven architectures. - Azure Cloud & Infrastructure - Deploy AI workloads on Azure App Services, Functions, AKS, Logic Apps, Event Grid. - Implement Infrastructure as Code (IaC) using Azure Bicep / ARM. - Ensure security, RBAC, Vault, secrets management, and runtime policies are enforced. - Improve cost, performance, observability, and reliability of AI workloads. - ML, Data & Observability - Build embeddings, feature pipelines, telemetry, and drift detection. - Conduct data analysis and experimentation using KQL and Splunk SPL. - Support model selection, fine-tuning strategies, safety constraints, and evaluation frameworks. - Ownership - Translate ambiguous needs into measurable AI workflows. - Collaborate with security, engineering, and product teams. - Produce clear documentation including architecture, prompts, and runbooks. - Act as an internal subject matter expert for agentic systems and modern AI development patterns. Required Skills: - Advanced prompt engineering skills with Gemini, GPT, Claude, and multi-model systems. - Proficiency in Python, FastAPI, async programming. - 5+ years of experience with Azure cloud services and serverless patterns. - Hands-on experience with RAG and vector databases like Azure Search, Pinecone, FAISS. - Familiarity with agent frameworks such as LangChain, LlamaIndex, AutoGen, or custom. - Frontend experience with React + TypeScript. - Knowledge of CI/CD, GitHub Actions, and DevOps practices. Good to have: - Experience with multi-agent or autonomous systems. - Exposure to AI in security operations or automation. - Certifications in Azure / ML Ops or SOC2 / ISO / AI governance. - Contributions to open-source or AI tooling. This is a hybrid remote/in-office role that offers various benefits beyond the basics to support your well-being and professional growth. Cyderes is committed to being an Equal Opportunity Employer and values diversity in the workplace. If you are passionate about developing cutting-edge AI systems and want to work in a supportive environment, then this role could be the perfect fit for you. As a Full-Stack AI Engineer at Cyderes, you will play a crucial role in developing production-grade agentic AI systems. Your focus will be on prompt engineering, Python backend services, Azure-native cloud architecture, and full-stack delivery of reliable AI workflows at scale. You will report to the Engineering Manager for seamless coordination. Responsibilities: - Prompt Engineering & Agentic Systems - Design, test, and enhance enterprise-grade prompts, system instructions, schemas, and agent workflows. - Build multi-step agentic pipelines, self-refining loops, and deterministic fallback logic. - Implement PromptOps practices such as versioning, evaluations, regression testing, token optimization, and governance. - Develop RAG pipelines, vector search, memory layers, and grounding strategies. - Backend & Full-Stack Engineering - Create Python-based microservices using FastAPI, async patterns, and REST APIs. - Deliver full-stack AI applications (React, TypeScript) for multi-turn AI interactions. - Manage GitHub, CI/CD, testing, branching, and release automation. - Integrate AI services through serverless, containerized, and event-driven architectures. - Azure Cloud & Infrastructure - Deploy AI workloads on Azure App Services, Functions, AKS, Logic Apps, Event Grid. - Implement Infrastructure as Code (IaC) using Azure Bicep / ARM. - Ensure security, RBAC, Vault, secrets management, and runtime policies are enforced. - Improve cost, performance, observability, and reliability of AI workloads. - ML, Data & Observability - Build embeddings, feature pipelines, telemetry, and drift detection. - Conduct data analy