Senior Software Engineer

Lead the end-to-end architecture, development, deployment, and operation of complex software and AI solutions. Build production-grade copilots, autonomous agents, multi-agent workflows, and retrieval-augmented generation systems. Establish reusable architecture patterns, frameworks, APIs, and platform capabilities that accelerate AI adoption across teams. Design secure integration with enterprise applications, operational workflows, and structured and unstructured data sources. Establish evaluation frameworks covering accuracy, relevance, safety, latency, reliability, user experience, and cost-effectiveness. Implement comprehensive observability for software, models, and agents, monitoring for drift, failures, and quality degradation. Make architectural decisions regarding model selection, orchestration, scalability, resiliency, and cost optimization. Apply responsible AI principles, ensuring security, privacy, compliance, and data governance in all engineering processes. Mitigate risks related to prompt injection, data leakage, hallucinations, and dependency integration. Collaborate with cross-functional teams to resolve complex technical challenges and deliver measurable outcomes. Bachelor's Degree in Computer Science or related technical field and 7+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent practical experience. 7+ years of professional software-engineering experience. Advanced proficiency in one or more languages such as C#, Python, Java, JavaScript, or TypeScript. Experience designing and operating distributed cloud services or enterprise-scale platforms. Understanding of system design, APIs, data architecture, resiliency, security, and observability. Practical experience building AI-enabled applications using LLMs, prompt engineering, embeddings, and agentic workflows. Familiarity with AI platforms like Azure OpenAI, Semantic Kernel, or AutoGen. Knowledge of AI evaluation, responsible-AI controls, and defense mechanisms. Experience influencing stakeholders and developing shared engineering frameworks. Demonstrated impact across multiple teams or business processes.