Founding AI Platform Engineer

Role Overview: As a hands-on technical co-founder at Aegis, you will be responsible for designing, building, and shipping the first MVP of the AI security and governance platform. This role involves a combination of full-stack engineering, AI/ML systems, cloud infrastructure, data pipelines, and security-focused product thinking. Key Responsibilities: - Own the end-to-end Aegis Discovery MVP, including: - AI agent discovery across cloud, repositories, SaaS logs, and runtime telemetry - Data normalization pipeline using OpenTelemetry-style schemas - Agent identity correlation across runtime, cloud, repo, and SaaS signals - Agent fingerprinting to identify frameworks such as LangChain, CrewAI, LlamaIndex, AutoGen, and direct LLM SDK usage - Risk scoring based on data sensitivity, autonomy, access scope, and behavior drift - Policy recommendation logic using capability matching and policy templates - Minimal discovery console to show agents, risk findings, and recommended policies - Integration with existing Aegis runtime and SDK for policy enforcement Qualifications Required: - Ideal candidate should have 45 years of strong hands-on engineering experience with practical exposure to: - Full-stack development: backend APIs, frontend UI, databases, and deployment - Python and/or TypeScript - AI/LLM application development using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, OpenAI, Anthropic, or similar - Cloud platforms, especially AWS - Kubernetes, Docker, and basic DevOps - PostgreSQL or graph-style data modeling - Event pipelines, logs, telemetry, or observability systems - Security concepts such as IAM, service accounts, audit logs, data sensitivity, and policy enforcement - Building fast MVPs with clean architecture and practical trade-offs Additional details of the company: Aegis is building an AI security and governance platform focused on discovering, classifying, risk-scoring, and enforcing policies for AI agents, AI scripts, and automation workflows across enterprise environments. The MVP goal is to produce a real agent record, real risk findings, and a real policy recommendation from an end-to-end demo path, not mocked outputs. The ideal candidate should also have experience with tools like OpenTelemetry, eBPF, Pixie, CloudQuery, Vector, Semgrep, Apache Flink, OPA/Rego, or similar tools, and experience in building security, governance, compliance, or developer infrastructure products. Familiarity with AI agent risks, shadow AI, data leakage, PHI/PII, and enterprise AI governance is a plus. Note: This test must be taken to be considered for this position. Role Overview: As a hands-on technical co-founder at Aegis, you will be responsible for designing, building, and shipping the first MVP of the AI security and governance platform. This role involves a combination of full-stack engineering, AI/ML systems, cloud infrastructure, data pipelines, and security-focused product thinking. Key Responsibilities: - Own the end-to-end Aegis Discovery MVP, including: - AI agent discovery across cloud, repositories, SaaS logs, and runtime telemetry - Data normalization pipeline using OpenTelemetry-style schemas - Agent identity correlation across runtime, cloud, repo, and SaaS signals - Agent fingerprinting to identify frameworks such as LangChain, CrewAI, LlamaIndex, AutoGen, and direct LLM SDK usage - Risk scoring based on data sensitivity, autonomy, access scope, and behavior drift - Policy recommendation logic using capability matching and policy templates - Minimal discovery console to show agents, risk findings, and recommended policies - Integration with existing Aegis runtime and SDK for policy enforcement Qualifications Required: - Ideal candidate should have 45 years of strong hands-on engineering experience with practical exposure to: - Full-stack development: backend APIs, frontend UI, databases, and deployment - Python and/or TypeScript - AI/LLM application development using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, OpenAI, Anthropic, or similar - Cloud platforms, especially AWS - Kubernetes, Docker, and basic DevOps - PostgreSQL or graph-style data modeling - Event pipelines, logs, telemetry, or observability systems - Security concepts such as IAM, service accounts, audit logs, data sensitivity, and policy enforcement - Building fast MVPs with clean architecture and practical trade-offs Additional details of the company: Aegis is building an AI security and governance platform focused on discovering, classifying, risk-scoring, and enforcing policies for AI agents, AI scripts, and automation workflows across enterprise environments. The MVP goal is to produce a real agent record, real risk findings, and a real policy recommendation from an end-to-end demo path, not mocked outputs. The ideal candidate should also have experience with tools like OpenTelemetry, eBPF, Pixie, CloudQuery, Vector, Semgre