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Apply to Full-Stack AI Engineer (Agentic Solutions) | Remote | USC Only.
Experience: 6+ years in software engineering, including 2+ year building LLM or agent solutions
The role You build, evaluate and deploy agentic solutions from an approved spec, using coding agents for most code, tests and documentation. You direct the agents, check their output and own the result. You deliver working software that is instrumented, evaluated, and ready for the business to accept.
What you will do
Size stories with the product owner, flag effort and risks, and confirm data and access early.
Test the spec for buildability, prepare the repo and agent context, and record open questions.
Slice stories into tasks, estimate in story points and commit the sprint with the POD.
Build with coding agents: code, unit and integration tests, and documentation.
Implement agents on Copilot Studio, Azure AI Foundry Agents or Microsoft Agent Framework, including prompts, tools, connectors, workflows and integrations.
Build the data and retrieval layer:
Document extraction pipelines with Azure AI Document Intelligence, including validation and confidence handling.
Chunking, embedding, indexing and hybrid search in a vector store such as Azure AI Search.
Calls to decision and rules APIs, with the LLM handling interpretation and drafting only.
Apply context engineering: write and version prompts and instructions, manage context windows, memory and tool results, and keep token use within budget.
Handle LLM calls reliably: structured outputs, retries, fallbacks, caching and rate limits.
Build evaluation sets and run evals on every release: automated metrics, LLM-as-judge and human review samples. Fix findings and resubmit.
Instrument every agent with OpenTelemetry. Build traces, dashboards and alerts for latency, token use, cost, errors and quality.
Run A/B tests as the architect designs them: variant routing, metric capture and result readouts.
Deploy through CI/CD, compile release evidence for go/no-go, and support the business through validation and handover.
Must have
Strong full-stack engineering in Python and/or C#/.NET, plus API and integration development.
Hands-on delivery of LLM or agent applications: prompt design, function and tool calling, RAG and workflow orchestration.
Practical experience with at least one of Azure AI Foundry, Copilot Studio or Microsoft Agent Framework.
Experience with a document intelligence service (Azure AI Document Intelligence or similar) on real, messy documents.
Experience with a vector database or search index: embeddings, chunking, hybrid search and relevance tuning.
Working knowledge of context engineering and token handling.
Evaluation practice: building golden datasets, running evals in CI, and interpreting results for non-deterministic output.
OpenTelemetry instrumentation and use of Azure Monitor, Application Insights or an equivalent observability tool to diagnose agent failures.
Familiarity with A/B testing, feature flags and traffic splitting.
Daily use of AI coding agents (GitHub Copilot, Claude Code, Cursor or similar). You write clear specs and context files, review agent code critically and catch its mistakes.
CI/CD, Git workflows, and Azure fundamentals (identity, secrets, networking, monitoring).
Clear communication with business users and daily Agile collaboration.
Microsoft certifications (Azure AI Engineer Associate / Azure AI Apps and Agents Developer Associate)
Nice to have
Natural-language-to-data solutions and data pipelines (Azure Data Factory, Databricks or Fabric, SQL).
Rules engines or workflow automation (Power Automate, Logic Apps).
Healthcare or payer exposure, including PHI handling.
The role You build, evaluate and deploy agentic solutions from an approved spec, using coding agents for most code, tests and documentation. You direct the agents, check their output and own the result. You deliver working software that is instrumented, evaluated, and ready for the business to accept.
What you will do
Size stories with the product owner, flag effort and risks, and confirm data and access early.
Test the spec for buildability, prepare the repo and agent context, and record open questions.
Slice stories into tasks, estimate in story points and commit the sprint with the POD.
Build with coding agents: code, unit and integration tests, and documentation.
Implement agents on Copilot Studio, Azure AI Foundry Agents or Microsoft Agent Framework, including prompts, tools, connectors, workflows and integrations.
Build the data and retrieval layer:
Document extraction pipelines with Azure AI Document Intelligence, including validation and confidence handling.
Chunking, embedding, indexing and hybrid search in a vector store such as Azure AI Search.
Calls to decision and rules APIs, with the LLM handling interpretation and drafting only.
Apply context engineering: write and version prompts and instructions, manage context windows, memory and tool results, and keep token use within budget.
Handle LLM calls reliably: structured outputs, retries, fallbacks, caching and rate limits.
Build evaluation sets and run evals on every release: automated metrics, LLM-as-judge and human review samples. Fix findings and resubmit.
Instrument every agent with OpenTelemetry. Build traces, dashboards and alerts for latency, token use, cost, errors and quality.
Run A/B tests as the architect designs them: variant routing, metric capture and result readouts.
Deploy through CI/CD, compile release evidence for go/no-go, and support the business through validation and handover.
Must have
Strong full-stack engineering in Python and/or C#/.NET, plus API and integration development.
Hands-on delivery of LLM or agent applications: prompt design, function and tool calling, RAG and workflow orchestration.
Practical experience with at least one of Azure AI Foundry, Copilot Studio or Microsoft Agent Framework.
Experience with a document intelligence service (Azure AI Document Intelligence or similar) on real, messy documents.
Experience with a vector database or search index: embeddings, chunking, hybrid search and relevance tuning.
Working knowledge of context engineering and token handling.
Evaluation practice: building golden datasets, running evals in CI, and interpreting results for non-deterministic output.
OpenTelemetry instrumentation and use of Azure Monitor, Application Insights or an equivalent observability tool to diagnose agent failures.
Familiarity with A/B testing, feature flags and traffic splitting.
Daily use of AI coding agents (GitHub Copilot, Claude Code, Cursor or similar). You write clear specs and context files, review agent code critically and catch its mistakes.
CI/CD, Git workflows, and Azure fundamentals (identity, secrets, networking, monitoring).
Clear communication with business users and daily Agile collaboration.
Microsoft certifications (Azure AI Engineer Associate / Azure AI Apps and Agents Developer Associate)
Nice to have
Natural-language-to-data solutions and data pipelines (Azure Data Factory, Databricks or Fabric, SQL).
Rules engines or workflow automation (Power Automate, Logic Apps).
Healthcare or payer exposure, including PHI handling.