Overview

Location: India, Remote-First

Responsibilities

• Design and build LLM-powered analysis and classification pipelines, then productionize them as Go services.
• Prototype approaches in Python, including prompting strategies, RAG, structured extraction, and ML classifiers, and ship solutions that meet defined accuracy targets.
• Define ground-truth datasets, evaluation metrics, and regression suites to continuously measure and improve model quality.
• Monitor model quality and drift in production and build processes to identify and address degradation.
• Collaborate with security researchers to translate attack patterns and risk signals into detection and summarization logic.
• Integrate AI-powered capabilities with event, storage, and UI layers to surface actionable results to security teams.
• Use AI-assisted development workflows to accelerate implementation, testing, debugging, and experimentation.

Qualifications

• Agentic AI Forensics: Turn agent traces, prompts, tool calls, and security events into structured, explainable findings for security analysts using LLM reasoning, RAG, structured extraction, and rigorous evaluation.
• Cloud Discovery: Classify cloud resources and workloads to identify AI agents, model endpoints, and AI-enabled applications, while inferring their purpose and security risk using LLM/ML classification at scale.
• 4+ years of software engineering experience, including 2+ years shipping LLM- or ML-backed features to production.
• Strong Go skills for production backend services and strong Python skills for experimentation and data pipelines.
• Hands-on experience with LLM APIs, prompt engineering, structured outputs, and RAG.
• Understanding of AI agent architectures, including tool calling, MCP or similar protocols, multi-step planning, and common failure modes.
• Fluent English with strong written and verbal communication skills.
• Comfortable using modern AI coding assistants such as Claude Code, Cursor, GitHub Copilot, Codex, or similar. This is a must-have.
• Strong ownership and the ability to work independently in a remote-first, distributed environment.
• Familiarity with tracing AI applications and OpenTelemetry-style observability.
• Previous experience in cybersecurity, security tooling, or trust & safety.
• Work on a greenfield product at the intersection of cybersecurity and agentic AI.
• Turn cutting-edge LLM/ML approaches into production systems protecting enterprise customers.
• Work across both AI experimentation and production engineering, from Python prototypes to Go services.
• Take ownership of a key workstream and influence architecture from an early stage.
• Collaborate with a highly technical, distributed team where AI is a core part of the development process.

Nice to have

• Background in security analytics, SIEM/SOAR, or digital forensics.
• Practical knowledge of major cloud provider APIs, IAM models, and resource inventory.