Full Stack Engineer

Knightscope•Sunnyvale, CA

7d•$175,000 - $200,000•Onsite

About The Position

Knightscope is seeking a Full Stack Engineer to own the engineering lifecycle of all GenAI-powered applications built and used by Knightscope employees. This role is responsible for designing, developing, securing, and maintaining internal AI tools that enhance operational efficiency. The engineer will be the central technical owner, ensuring that all internal GenAI applications are built with security, scalability, and responsible AI principles. The ideal candidate has full stack engineering expertise, a deep understanding of the Secure Software Development Lifecycle (SSDLC), and experience integrating large language models and AI APIs into production applications. This is a full-time, on-site role at Sunnyvale HQ.

Requirements

4–8 years of full stack software engineering experience.
At least 2 years working directly with AI/ML APIs or LLM-based applications in a production environment.
Bachelor’s degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience).
Demonstrated experience owning multiple software projects end-to-end.
Proficiency in front-end development: React, TypeScript, Next.js, or equivalent modern frameworks.
Strong back-end skills: Python and/or Node.js; experience with REST and GraphQL APIs; familiarity with FastAPI, Flask, Express, or similar frameworks.
Hands-on experience with LLM/GenAI integration: OpenAI API, Anthropic Claude API, Langchain, LlamaIndex, or equivalent frameworks.
Experience with vector databases (Pinecone, Weaviate, pgvector, or equivalent) and RAG architecture patterns.
Cloud platform experience (AWS, GCP, or Azure): containerization (Docker, Kubernetes), serverless functions, managed databases, and IAM/secrets management.
Strong understanding of the Secure Software Development Lifecycle (SSDLC), including threat modeling, OWASP Top 10, SAST/DAST tooling, and secrets management.
Familiarity with AI-specific security risks: prompt injection, data exfiltration, model inversion, and jailbreak mitigation strategies.
Proficiency with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or equivalent) and DevSecOps practices.
Solid understanding of database design: relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis).

Nice To Haves

Security certifications such as CSSLP, CompTIA Security+, AWS Security Specialty, or equivalent.
Experience building internal developer platforms, tooling portals, or enterprise AI application hubs.
Background in robotics, IoT, or physical security technology environments.
Familiarity with AI governance frameworks, responsible AI principles, and emerging regulations (e.g., EU AI Act, NIST AI RMF).
Experience with observability tooling (Datadog, Grafana, OpenTelemetry) and monitoring LLM application performance and costs.

Responsibilities

Serve as the technical owner and primary maintainer of all internal GenAI/AI-powered applications across the company.
Maintain a centralized registry of all employee-built GenAI tools, ensuring visibility, governance, and version control.
Evaluate, onboard, and integrate third-party AI APIs and open-source models into internal tooling.
Partner with cross-functional stakeholders to scope, prioritize, and deliver new AI-powered features and tools.
Design and build scalable, maintainable front-end interfaces (React, Next.js, or equivalent) for internal AI tools and dashboards.
Develop and maintain back-end APIs, microservices, and data pipelines (Python, Node.js, or equivalent) to support AI model inference, data retrieval, and workflow automation.
Architect and manage cloud infrastructure (AWS, GCP, or Azure) to host and scale GenAI applications.
Implement RAG (Retrieval-Augmented Generation) pipelines, vector databases, and prompt engineering frameworks.
Champion and enforce SSDLC best practices across all GenAI projects, including threat modeling, secure code reviews, SAST/DAST, and dependency scanning.
Design authentication, authorization, and data privacy controls for all AI applications.
Identify and remediate prompt injection vulnerabilities, data leakage risks, and model misuse scenarios.
Establish CI/CD pipelines with integrated security gates, automated testing, and deployment guardrails.
Conduct regular security audits and vulnerability assessments of GenAI applications and maintain a risk register.
Define and document internal standards for AI application development.
Review and approve employee-built AI applications prior to production deployment.
Monitor AI application usage, performance, and costs, and provide regular reporting to leadership.
Stay current with the rapidly evolving GenAI landscape and proactively recommend new tools, frameworks, or approaches.