Senior DevOps / SRE Engineer

Staff Software Engineer - Backend & AI Infra Remote | Full-time Location: Based in US to GMT timezones Compensation: Competitive Compensation Package Our client is a high-growth technology firm. They are seeking a Staff Software Engineer to spearhead two critical domains: the core agent runtime and backend infrastructure powering a high-frequency trading fleet, and the comprehensive migration of model hosting and agent deployment to in-house, proprietary infrastructure. This is a foundational, high-impact building role. The successful candidate will design and implement the backend services, runtime engines, and deployment systems that enable a fleet of autonomous agents to operate with superior speed, reliability, and intelligence. By moving away from third-party LLM providers and hosted platforms, this role will establish the sovereign infrastructure necessary for the next generation of autonomous financial software. Key Responsibilities Agent Runtime & Backend Development Plugin Runtime Ownership: Lead the evolution of the per-agent process, migrating from a distributed Go/Python hybrid to a centralized, high-performance Go service utilizing Postgres state and real-time websocket price feeds. Rules Engine Engineering: Build a YAML-configurable "Scanner Gateway" to bridge signal production and execution, allowing for complex scoring and filtering without direct code manipulation. Advanced Execution Systems: Develop and maintain the RatchetStop Backend , a centralized profit-trailing service capable of sub-second evaluation and websocket-based order execution to protect capital even when agents are offline. Data & Connectivity: Manage the Model Context Protocol (MCP) server bridging agents to platform tools, and oversee a high-throughput data pipeline (Redis, Postgres, ClickHouse) for real-time market intelligence ingestion. Model & Agent Hosting Migration Infrastructure Sovereignty: Lead the technical execution of migrating agents from third-party platforms to a custom-built, Senpi-hosted environment featuring isolated workspaces and state persistence. Model Serving: Evaluate and implement the transition from external LLM APIs (Anthropic, Google) to self-hosted inference, optimizing for telemetry capture and performance. Telemetry & Feedback Loops: Architect systems to capture every agent decision and score, creating a self-reinforcing loop where the fleet learns and improves from collective performance data. Deployment Pipelines: Build robust CI/CD pipelines for zero-downtime rollouts, ensuring that updates to scanner logic or runtime patches do not interrupt active market positions. Infrastructure & Operations System Reliability: Design monitoring and alerting frameworks to detect agent failures, state corruption, or authentication expirations before they impact financial performance. Cloud Orchestration: Manage AWS/EKS environments using Infrastructure-as-Code (IaC). Incident Response: Own the operational health of the fleet, acting as the primary responder for high-stakes trading system incidents. I Interview Process Founder / CEO Interview: Introduction to the vision and strategic goals. Take-Home Test: A practical assessment of technical design and coding capabilities. Technical Interview: A deep dive into systems architecture and engineering expertise. Final Interview: Cultural alignment and final technical synthesis. Requirements Technical Essentials Expert Backend Engineering: Proficiency in writing production-grade code in Go , Python , and Node.js/TypeScript (Go is strongly preferred for runtime services). Startup Experience: A proven track record of building complex backend services (APIs, job scheduling, distributed systems) from scratch in a fast-paced environment. Real-Time Systems: Deep understanding of low-latency environments, websocket management, and sub-second condition evaluation. Database Mastery: Production experience with Postgres , Redis , and at least one analytical database (e.g., ClickHouse , TimescaleDB, or BigQuery). Orchestration: Hands-on experience deploying, scaling, and debugging production workloads on Kubernetes (AWS EKS) . End-to-End Ownership: Demonstrated ability to design, build, deploy, and maintain systems throughout their entire lifecycle. Preferred Qualifications LLM Infrastructure: Experience with model serving and optimizing inference (e.g., vLLM, TGI, or TensorRT-LLM). FinTech/Trading: Background in exchange APIs, wallet operations, or on-chain infrastructure where uptime has direct financial consequences. Agentic Frameworks: Familiarity with Model Context Protocol (MCP) or orchestrating multi-agent platforms. Benefits Competitive compensation and equity packages. The opportunity to build foundational infrastructure in a new category of autonomous software. High-autonomy environment with a focus on engineering excellence. Collaborative culture working alongside industry-leading founders and engineers. Due to the high volume of applications we anticipate, we