Applied AI Engineer II

Translate product specifications into AI service architectures — decompose business intent into agent workflows, data source integrations, and scalable service designs.

Develop and iterate on LLM-based solutions including prompt engineering, model selection, cost/token optimization, and RAG pipeline design.

Build evaluation systems including rubrics, golden datasets, and judge agents to validate agent correctness and safety before production deployment.

Write production-quality C# and Python with test-driven development, ensuring services meet reliability, performance, and security standards.

Deploy services using CI/CD pipelines, feature flags, and staged rollouts with full production observability (tracing, logging, metrics).

Implement secure service patterns including RBAC, Managed Identities, and secrets management.

Integrate agents with Azure data sources and cloud-native services to ground agent responses in real-time signals.

Apply Responsible AI practices across all agent development, ensuring outputs are safe, fair, and compliant.

Execute reliably within sprint and co-development commitments across ACES and partner engineering teams.

Own the end-to-end lifecycle of AI service components, including design, development, testing, deployment, monitoring, and incident response.

Bachelor's degree in Computer Science, Engineering, or related field. 3+ years of software engineering experience in C# and/or Python, with production deployment experience.

building or integrating with LLM-based systems (prompt engineering, RAG, or agent frameworks).

Familiarity with Azure services (Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB, or similar cloud-native technologies).

with CI/CD pipelines, automated testing, and production observability.

Understanding of data structures, algorithms, and system design fundamentals.

Bachelor's or Master's degree in Computer Science, AI, or related field.

with multi-agent orchestration and agent-to-agent communication protocols.

with evaluation frameworks for AI systems (rubrics, golden datasets, judge agents).

Familiarity with RAG pipelines and vector retrieval systems.

with container orchestration, microservices architecture, and cloud-native deployment patterns.

with security practices (RBAC, Managed Identities, secrets management).

working in Agile/sprint-based delivery with cross-org dependencies.

Problem-solving skills and ability to thrive in a fast-paced, collaborative environment.