Senior Software Engineer

About the job

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, groundedness, relevance, safety, latency, reliability, user experience, and cost. Implement comprehensive observability for applications, models, and agents, including drift, failures, anomalous behavior, and quality degradation. Make architectural decisions involving model selection, orchestration, data retrieval, memory, caching, scalability, resiliency, and cost optimization. Apply responsible-AI, security, privacy, compliance, accessibility, and data-governance requirements throughout the engineering lifecycle. Anticipate and mitigate risks such as prompt injection, data leakage, excessive permissions, hallucinations, unsafe actions, and model dependency. Lead technical investigations and resolution of complex production incidents. Partner with product, data science, security, compliance, and business teams to convert ambiguous opportunities into executable technical strategies. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Advanced proficiency in one or more languages such as C#, Python, Java, JavaScript, or TypeScript. Experience designing, delivering, and operating distributed cloud services or enterprise-scale platforms. Practical experience building AI-enabled applications using LLMs, prompt engineering, embeddings, vector search, RAG, and agentic workflows. Strong understanding of system design, distributed systems, APIs, data architecture, resiliency, security, performance, and observability. Experience with Azure OpenAI, Azure AI Foundry, Semantic Kernel, AutoGen, or comparable AI platforms and frameworks. Experience deploying enterprise-scale copilots, AI agents, RAG platforms, or multi-agent systems into production. Knowledge of AI evaluation, red-team testing, responsible-AI controls, content safety, prompt-injection defenses, and human-in-the-loop mechanisms. Experience with Kubernetes, containers, infrastructure as code, managed identities, private networking, and Zero Trust architecture. Familiarity with MLOps, LLMOps, model lifecycle management, experimentation, and AI telemetry. Experience with supply-chain, ERP, SAP, or other mission-critical enterprise platforms. Experience creating shared engineering frameworks or influencing architecture across organizational boundaries. Influences senior stakeholders and aligns teams around a coherent technical strategy. Develops other senior engineers and raises the engineering capability of the broader organization. Delivers measurable impact across multiple products, teams, or business processes—not only individual features.