Implement and maintain LLM and SLM pipelines, including prompt engineering, inference, and evaluation.
Support RAG pipelines, embeddings, and retrieval systems used in intelligence applications.
Assist in building agent workflows that automate analytical or operational tasks.
Write clean, maintainable code (Python or others) to support AI services and integrations.
Contribute to AI evaluation, testing, and hallucination-mitigation techniques.
Use AI-assisted development tools (e.g., Copilot, Cursor) to improve development velocity and quality.
Collaborate with Product and Engineering teams to integrate AI capabilities into user-facing workflows.
Follow established AI governance, safety, and cost-optimization practices.

5+ years of experience in software engineering, machine learning, or applied AI roles.
Hands-on experience working with LLMs and/or SLMs, including prompting, inference, or fine-tuning.
Experience building or contributing to RAG pipelines, embeddings, or retrieval systems.
Familiarity with agent-based systems or workflow automation (academic, professional, or open-source).
Strong programming skills in Python; experience with common libraries (PyTorch, TensorFlow, etc.).
Solid foundation in machine learning concepts (training, evaluation, overfitting, metrics).
Experience using or contributing to modern AI/ML tooling and workflows (ML pipelines, evaluation scripts, model serving).
Ability to work in a collaborative, fast-moving, mission-driven environment.
Experience applying AI to intelligence, investigative, analytical, or risk-related applications (preferred).
Familiarity with vector databases, graph databases, or knowledge graphs (preferred).

Generative AI Engineer, Agentic

Key skills