Design, implement, and evolve RAG pipelines combining structured data, embeddings, and LLMs.
Develop and maintain prompt strategies used across multi-step agent workflows.
Integrate LLMs into production systems with attention to reliability, cost, and latency.
Orchestrate AI-driven workflows across internal services and third-party APIs.
Define and improve practices for prompt versioning, evaluation, and iteration.
Work closely with platform engineers to ensure AI systems are maintainable and observable.
Help the team reason about when deeper customization or model-level work may be warranted in the future.

Requirements

5+ years of professional software engineering experience.
Strong backend experience with modern .NET and Python.
Demonstrated, hands-on experience with:
Retrieval-Augmented Generation (RAG)
Prompt engineering beyond simple completion calls
Vector databases and embedding workflows
API-driven LLM integrations
Experience integrating AI systems into real products, not just prototypes.
Ability to reason about trade-offs: determinism, accuracy, cost, and performance.
Strong communication skills and the ability to explain AI behavior clearly.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

.NETPythonRetrieval-Augmented Generation (RAG)prompt engineeringvector databasesembedding workflowsAPI-driven LLM integrationsAI system integrationsoftware engineeringmulti-step agent workflows

Soft skills

strong communication skillsreasoning about trade-offsexplain AI behavior clearly