Generative & Agentic AI Engineer

Overview

Babel Street is the trusted technology partner for the world’s most advanced identity intelligence and risk operations. We deliver advanced AI and data analytics solutions providing unmatched, analysis-ready data regardless of language, proactive risk identification, 360-degree insights, high-speed automation, and seamless integration into existing systems. Babel Street empowers government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage.

The actionable insights we deliver safeguard lives and protect critical assets around the world. Babel Street is headquartered in Reston, Virginia, with regional offices in Boston, MA and Cleveland, OH, and international offices in Australia, Canada, Israel, Japan, and the U.K. For more information, visit

As an Engineer on the Generative & Agentic AI team, you will play a hands-on role in building, deploying, and operating AI capabilities that power Babel Street’s intelligence applications. You will work closely with senior AI leaders, product teams, and engineers to implement generative and agentic AI solutions that support investigative, analytical, and operational workflows across the platform.

This is an execution-focused role for an engineer with strong foundations in machine learning and generative AI who is excited to work on real-world, mission-driven applications. You will contribute directly to LLM and SLM pipelines, retrieval and grounding systems, agent workflows, and AI-enabled features—while learning how to deliver AI that is safe, reliable, cost-efficient, and production-ready.

This is a hybrid role to be based out of either our Reston, VA/Washington DC office or our Somerville MA office.

This role spans three practical execution areas:

LLM- and SLM-based systems – you will help implement and operate 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 development lifecycle & governance – you will help product ionize 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.

• 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, Tensor Flow, etc.).
• Solid foundation in machine learning concepts (training, evaluation, overfitting, metrics).
• Exp…