Senior AI Engineer — Agentic Systems & LLMs

Recorded Future; Boston, Northern (MA, KY); Hybrid; USD 129,000 - 194,000; Full time

Benefits offered by this job

Incentive compensation

Equity

Medical

Dental

Vision

Life insurance

401K

Job summary

Recorded Future is seeking a Senior Software Engineer to design, build, and deploy AI Agentic systems that enrich threat intelligence. You will mentor junior engineers and drive architectural decisions across global teams.

You will work with Python and cloud platforms to ship production-grade software using LLMs, Agent frameworks, and retrieval-augmented generation (RAG) techniques in a fast-paced environment.

Qualifications

• Experience designing and delivering scalable software systems.
• Strong background in Python for production AI/ML.
• Experience with LLMs, agentic tooling, and RAG.

Responsibilities

• Design, develop, and deploy highly reliable and scalable software systems, specializing in AI Agentic tooling and ML applications.
• Serve as a technical leader in frameworks and best practices for autonomous AI agents in production.
• Collaborate with engineering managers to integrate AI Agent solutions across the platform.
• Drive engineering excellence through code reviews, performance optimization, and operational maturity.
• Lead the technical design and architectural discussions for new features within the AI Tooling roadmap.
• Manage project tasks through an agile SDLC, ensuring timely delivery and high-quality results.

Skills

Python

Distributed systems

AWS

GCP

Azure

Jira

Git

LLMs

Prompt engineering

Cross-functional teamwork

Tools

AWS

GCP

Azure

Jira

Git

Job description

Recorded Future is seeking a Senior Software Engineer to design, build, and deploy AI Agentic systems that enrich threat intelligence. You will mentor junior engineers and drive architectural decisions across global teams.

You will work with Python and cloud platforms to ship production-grade software using LLMs, Agent frameworks, and retrieval-augmented generation (RAG) techniques in a fast-paced environment.