hatch I.T. is partnering with Holokai to find a Senior Backend Engineer. See details below: About the Role:  Holokai is looking for a backend engineer to build core product alongside their co-founders and senior engineering team. You won’t pull pre-scoped tickets from a backlog. You’ll work with their senior engineers and leadership to decide what to build, then build it well. The Holokai platform spans a user desktop application, policy enforcement engine, an agentic workflow orchestrator, a model-agnostic gateway, and a SaaS management console. You’ll work across all of it, primarily in TypeScript and Go with some Python. The hard parts are genuinely hard, including sub-100ms policy evaluation, multi-model orchestration, and enterprise-grade security. They want someone who stays clear-headed in that complexity. They need engineers who have built and owned critical production running at real scale to serve exacting enterprise customers. That experience leaves a mark: the battle scars that keep you from over-engineering at the expense of reliability and pragmatism. They're heavy AI users in how they build, not just in what they ship. AI-assisted development is core to how this team works; they want someone who puts AI to work but catches it when it’s about to steer them wrong. Write simple code, ship often, and own what you put in production.   About Holokai: Holokai is building a platform to accelerate the secure, rational adoption of Enterprise AI. Their product enables organizations to safely deploy AI tools through model access controls, governance guards, and policy enforcement — while still delivering strong user productivity through a desktop chat experience and integrated AI workflows. They are an early-stage startup with pilot customers, rapid product iteration, and direct customer feedback loops. Designers at Holokai work close to product decisions and engineering implementation, not as a downstream function but as a core part of product creation. WHAT YOU'LL WORK ON: Backend services: APIs, data pipelines, integrations, and core business logic Policy enforcement: deterministic evaluation engines, rules processing, and classification Agentic orchestration: multi-step AI plans using LLM’s, executed as deterministic workflows with policy checkpoints built in Model gateway: routing and translation across LLM providers, token management, and request handling Data layer: PostgreSQL schema design, query optimization, and data modeling Production ownership: you monitor, debug, and fix what you ship REQUIRED SKILLS: Strong production experience in TypeScript and Go (Python a plus): real services running in production, not scripts and notebooks Designing and building APIs (REST, gRPC, or similar) and backend service architecture Relational databases, PostgreSQL: schema design, query performance, migrations Reading and extending code you didn’t write, quickly and confidently Fluent with AI-assisted development tools (Claude Code, Cursor, or similar); you use them daily and they make you meaningfully faster Production experience with AWS Containerized deployments (Docker, ECS, EKS, or similar) A bias for shipping working software over polishing design docs Self-directed and comfortable with minimal structure in a fast-moving start-up environment U.S. Citizenship required Bachelor’s in Computer Science, Engineering, or a related field, or equivalent professional experience. NICE TO HAVE: LLM APIs and AI/ML pipelines: prompt engineering, model integration, token economics Policy engines or rules-based systems (OPA/Rego, decision engines, or similar) Message queues and event-driven architectures (RabbitMQ, SQS, Kafka) Frontend chops (React); not the focus, but handy for closing the loop on a feature Enterprise security, compliance, or governance experience (e.g. SOC 2, GDPR, etc.) WHO THRIVES HERE: Engineers who write code every day and want to keep it that way People who use AI tools seriously and keep hunting for the next edge Self-starters who take a problem, find the approach, and deliver without being told how People drawn to the applied science of AI who want to make enterprise AI work for real HOW WE WORK: At a start-up our size and stage, there's no living in a silo.  Engineering, product, sales, and customer success all own success together. We work hard and support each other, and no amount of great code makes up for being difficult to work with. We need curious people who have played the tech start-up game long enough to learn humility, drive and the genuine joy of working and succeeding on a great team.