Software Engineer II

About the job

Design, develop, test, deploy, and support scalable software services and AI-powered applications. Build copilots, intelligent agents, and workflow automation using large language models and agentic AI frameworks. Develop reusable APIs, services, libraries, and platform components for enterprise AI scenarios. Implement retrieval-augmented generation (RAG), semantic search, embeddings, tool calling, memory, and multi-agent workflows. Integrate AI capabilities with enterprise systems, operational processes, and structured and unstructured data sources. Establish automated evaluations for accuracy, groundedness, relevance, safety, latency, reliability, and cost. Write clean, maintainable, secure, accessible, and well-tested production code. Participate in architecture reviews, code reviews, incident response, troubleshooting, and operational improvements. Apply security, privacy, compliance, accessibility, and responsible-AI requirements throughout the engineering lifecycle. Use AI-assisted development tools to improve engineering velocity while maintaining accountability for code quality. Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Proficiency in one or more languages such as C#, Python, Java, JavaScript, or TypeScript. Experience designing and developing cloud-based applications, APIs, distributed services, or enterprise platforms. Understanding of object-oriented design, data structures, algorithms, design patterns, and system-design fundamentals. Practical knowledge of generative AI concepts, including LLMs, prompt engineering, embeddings, RAG, and intelligent agents. Strong problem-solving, debugging, communication, and cross-team collaboration skills. Experience with Azure OpenAI, Azure AI Foundry, Semantic Kernel, AutoGen, or similar technologies. Experience delivering production-grade copilots, AI agents, RAG systems, or intelligent automation. Knowledge of model and agent evaluation, responsible AI, prompt-injection protection, content safety, and AI red teaming. Familiarity with containers, Kubernetes, infrastructure as code, and managed-identity security patterns. Experience with microservices, event-driven architectures, distributed systems, caching, and performance optimization. Familiarity with MLOps, LLMOps, experimentation, telemetry, and AI lifecycle management. Experience with supply-chain, ERP, SAP, or other large-scale enterprise systems.