AI Engineer - Hyperproof

About Hyperproof

Hyperproof is on a mission to transform the Governance, Risk, and Compliance (GRC) world with a powerful new software platform. With Hyperproof, companies can save time and money while operating their programs at a higher level of effectiveness and accountability. We envision a world where organizations we depend on are truly trustworthy—and Hyperproof is the platform that will get them there.

We raised our Series B round in 2023 and continue to grow rapidly. We are seeking a driven Mid-Senior AI Engineer with a proven track record of building, deploying, and managing modern AI applications. As a key contributor to our engineering team, you will work closely with peers and our product team to develop AI-forward features that support our core platform. You will build services and intelligent agents that help our customers automate everyday tasks with high precision, verifiable results, and robust guardrails.

Who You Are

• You are an AI Engineer passionate about building advanced, intelligent services and systems.
• You have hands-on experience designing and delivering AI applications, managing agents, and optimizing vector databases.
• You understand that customer experience is critical and that AI features must deliver tangible business value.
• You thrive in a fast-paced environment and possess a deep understanding of constraint spaces and AI safety guardrails.

What You Will Do

• Build and Manage Agents: Design, deploy, and manage AI agents using modern agentic technologies, ensuring robust performance within defined constraint spaces and safety guardrails.
• RAG Optimization: Develop and manage complex RAG (Retrieval-Augmented Generation) pipelines, actively tuning and optimizing for high recall and precision.
• Spec-Driven Development: Partner closely with product managers to translate industry requirements into technical specifications, utilizing spec-driven development to build reliable AI features.
• Model Integration & Evals: Work with foundation models via APIs to enable use cases like document summarization and intelligent search. Establish evaluation design patterns to rigorously test, measure, and improve model outputs.
• Backend Architecture: Design and code robust backend architecture components heavily utilizing Postgres, REST APIs, queueing, storage systems, RBAC, encryption, and security.
• Quality & Operations: Write automated tests, manage version control and collaborative workflows via GitHub, and build monitoring and system health metrics.
• Continuous Learning: Stay up-to-date with AI advancements to ensure engineering practices remain at the leading edge of AI adoption.

What You Will Bring

• 4+ years of software engineering experience, including 18+ months working directly on AI-powered features, agents, or applications.
• Deep, practical experience using Claude Code.
• Hands-on experience building AI applications and integrating LLM-based features in production.
• Strong familiarity with RAG architectures (recall and precision metrics) and vector databases (e.g., Pinecone, Weaviate).
• Proven ability to build and manage AI agents, with a solid understanding of agent technologies, constraint spaces, and evaluation design patterns.
• Strong proficiency in backend development, featuring deep experience with Postgres and asynchronous programming (e.g., Node.js, TypeScript, Python, Go, and/or Java).
• Experience managing codebases and collaborative workflows using GitHub.
• Experience working with REST APIs using JSON and delivering scalable, reliable, maintainable software.
• Familiarity with Azure and/or AWS for deployment, debugging, and operations.
• Bachelor's degree in Computer Science, Mathematics, or a related field.

Location & Working Hours

• Location: Fully remote, working from India.
• Working Hours: Requires availability during afternoon and evening hours (India Standard Time) to facilitate effective collaboration with distributed engineering teams.

Candidate Experience & Interview Process

1. Take-Home Assessment
2. 30-minute initial chat with a Principal People & Talent Partner
3. Three 60-minute 1:1 interviews with engineering team members (including the hiring manager)

Note: Candidates must provide a valid non-VOIP phone number and remain on camera without filters during video interviews.