Generative & Agentic AI Engineer

Role Focus:

This role spans three practical execution areas:

Generative AI & Model Development

You will help implement and operate LLM- and SLM-based systems, contributing to prompt development, fine-tuning, evaluation, and inference optimization. You will support retrieval-augmented generation (RAG) pipelines, embeddings, and grounding techniques to ensure AI outputs are accurate, explainable, and aligned with intelligence use cases.

Agentic AI & Workflow Automation

You will assist in building and integrating agent-based workflows that automate analytical tasks, connect platform services, and support intelligence applications. This includes implementing agent logic, tool-use patterns, and basic orchestration under the guidance of senior engineers.

AI Engineering & Production Delivery

You will help productionize AI capabilities using modern AI SDLC tools and practices, contributing to evaluation, testing, telemetry, and guardrails that reduce hallucinations and ensure safe behavior. You will work within established governance frameworks to ensure AI features are measurable, reliable, and cost-aware.

What you will do:

• 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.

What you will bring:

Required

• 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.

Preferred

• Experience applying AI to intelligence, investigative, analytical, or risk-related applications.

• Familiarity with vector databases, graph databases, or knowledge graphs.

Education:

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required. 
Advanced degree is a plus but not required.