Software Engineer – Backend – Behavioral Security Products

Abnormal Security

. Design, build, and iterate on scalable backend and ML systems, APIs, frameworks, and internal tools .

Posted 9/16/2026full-timeRemote • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong backend proficiency in Python and experience with scalable application design, focusing on code quality, reliability, and performance. Capable of integrating LLM APIs and collaborating cross-functionally to drive project completion and technical improvements.

Highest-signal resume keywords

Python Backend DevelopmentAWS Cloud ServicesLLM API IntegrationSystem Design FundamentalsCode Quality and Reliability

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Backend DevelopmentPythonSystem DesignRelational DatabasesAWSGRPCKafkaRedisDockerKubernetes

Soft Skills

Proactive OwnershipCross-Functional CollaborationCommunication

Tools & Technologies

GenAI Coding AssistantsLLM APIsAgent FrameworksTechnical Documentation

Industry Keywords

Operational ExcellenceMonitoringIncident ResponsePrompt EngineeringAI-Driven Features

Tech Stack

Tools & technologies

AWSCloudDockerGRPCKafkaKubernetesPythonReactRedis

About the role

Key responsibilities & impact

Design, build, and iterate on scalable backend and ML systems, APIs, frameworks, and internal tools
Own well-scoped features and components, with guidance from senior engineers on complex cross-system work
Contribute to the stability, reliability, and operational excellence of critical systems
Write clean, testable, and resilient code with attention to edge cases and performance
Contribute to technical design documents and participate in design discussions
Participate in code and design reviews and contribute to on-call rotations
Help design and build LLM-powered features and agentic workflows for automated investigation, triage, or remediation assistants
Integrate LLM APIs and agent frameworks into backend services, considering reliability, cost, latency, and evaluation
Contribute to prompt design, tool/function-calling integrations, and guardrails for AI-driven components
Use GenAI coding assistants and agents in the development workflow to accelerate delivery and testing
Collaborate with product managers, designers, and engineers to align on specifications and priorities
Break down well-defined projects into executable steps and drive them to completion
Contribute to roadmap discussions and share ideas for technical improvements
Communicate updates, challenges, and solutions in an async-first environment
Seek feedback and mentorship from senior engineers

Requirements

What you’ll need

3–5 years of industry experience as a Software Engineer, with a track record of shipping production backend systems
Solid backend proficiency in Python, with experience building and maintaining production systems
Experience with system design fundamentals and building reliable, scalable applications
Working knowledge of relational databases and modern data storage technologies
Experience with AWS cloud services, including S3 and RDS, and deployment practices
Understanding of service health, monitoring, and incident response practices
Comfortable writing technical documentation and contributing to design discussions
Proactive ownership of assigned work and eagerness to grow into increasingly complex projects
Strong focus on code quality, reliability, monitoring, and performance
Ability to work cross-functionally and in a distributed environment
Genuine interest in LLM-powered features and agentic systems, and willingness to leverage GenAI assistants and coding agents
Exposure to service-to-service communication, such as gRPC and Kafka, and caching with Redis is a plus
Familiarity with containerization and orchestration, such as Docker, Kubernetes, and Helm, is a plus
Hands-on experience integrating LLM APIs, such as OpenAI and Anthropic, into production applications is nice to have
Exposure to agent frameworks or patterns, such as LangChain, LangGraph, tool/function calling, and ReAct-style agents, is nice to have
Familiarity with prompt engineering, evaluation, and observability for AI-driven features is nice to have

Benefits

Comp & perks

AI-assisted recruiting tools are used to prepare for candidate interviews, but do not make hiring decisions or screen candidates automatically
Pre-employment checks in line with prevailing legislation and Abnormal AI's security and privacy standards
Equal opportunity employer