Senior Software Engineer, AI DevEx

Job description

**This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Software Engineer, AI DevEx based in the United States.** This is an opportunity to shape the future of software development by building an AI-powered engineering platform that enables autonomous agents to handle much of the software delivery lifecycle. As a Senior Software Engineer, you will design the infrastructure, environments, integrations, and safeguards that allow AI agents to turn tickets, bug reports, support requests, and production alerts into tested, reviewable code. You will combine deep infrastructure expertise with hands-on experience using coding agents to improve engineering speed, quality, and reliability. Working within a distributed, remote-first team, you will help establish the architecture and principles that make AI-driven development scalable and secure. Your impact will be measured through faster pull request cycles, reduced review delays, and an increasing share of work completed with agent assistance. This role is ideal for an experienced engineer who enjoys experimentation, solving complex platform challenges, and building tools that meaningfully improve how teams work. ### Accountabilities - AI-Powered Developer Platform: Design and build the infrastructure that enables AI agents to participate in software development, from initial task intake through implementation, testing, and pull request review. - Development Environments: Build and maintain reproducible local development stacks and cloud-based coding environments that allow engineers and agents to run services and tests reliably without extensive manual setup. - Autonomous Coding Agents: Integrate and orchestrate AI agents that transform tickets, bug reports, and other requests into validated code changes and review-ready pull requests, including browser and native application testing where appropriate. - Agent Selection and Orchestration: Evaluate, select, and integrate open-source and commercial coding agents. Configure triggers, credentials, contextual information, tools, and execution workflows to ensure agents operate effectively and efficiently. - Workflow Integrations: Connect agent workflows to tools and systems such as Slack, Jira, GitHub, Datadog, and incident management platforms, enabling production alerts and operational issues to initiate potential fixes. - Infrastructure and Automation: Develop and improve cloud infrastructure, containers, CI/CD pipelines, and infrastructure-as-code solutions to support reliable, scalable, and repeatable agent execution. - Security and Governance: Establish guardrails governing agent access to data, secrets, and personally identifiable information. Define appropriate permissions, compliance controls, and human-review requirements for a regulated, real-money contest environment. - Continuous Improvement: Identify recurring issues that limit agent effectiveness, including missing context, unreliable tests, insufficient tools, and environment gaps, then address their root causes. - Agent Evaluation and Observability: Measure agent performance across coding and non-coding tasks, track execution quality and costs, and use telemetry and evaluation results to improve reliability and efficiency. - Business-Wide Enablement: Develop reusable agent runtimes, triggers, and safeguards that can support teams beyond engineering, helping other functions adopt automation with appropriate guidance. - Performance Measurement: Improve key indicators such as pull request cycle time, review latency, and the proportion of pull requests authored or reviewed by AI agents. - Cross-Team Collaboration: Work independently while communicating clearly across distributed teams, sharing progress, proactively identifying opportunities, and helping establish effective practices for AI-assisted development. ### Requirements - Professional Experience: At least six years of software engineering experience, including meaningful work in infrastructure, platform engineering, developer experience, or internal tooling. - Advanced AI Agent Usage: Current, hands-on experience using AI coding agents such as Claude Code, Codex, Cursor, or similar tools for at least 90% of your development work. You should understand their limitations and know how to improve their results. - Multi-Agent Orchestration: Experience coordinating multiple agents or delegating work to subagents is highly desirable, particularly when supported by custom skills, hooks, harness configurations, or parallel execution workflows. - Cloud Infrastructure: Strong experience with AWS services and infrastructure, including ECS/Fargate, RDS, Lambda, networking, and IAM. - Infrastructure as Code and Delivery: Practical experience with Terraform, AWS CDK, CloudFormation, or comparable infrastructure-as-code tools, along with containers and CI/CD pipelines that you have built, maintained, or optimized. - Software Development Skills: Ability to contribute directly to TypeScript and Node.js services and work comfortably within existing codebases. - Developer Tooling: Experience building internal platforms, development environments, automation systems, or other engineering tools that achieve real adoption among developers. - Data-Driven Evaluation: Familiarity with assessing AI agents across coding and non-coding tasks, interpreting performance data, and using repeatable evaluations to guide technical decisions. - Autonomy and Initiative: Ability to identify problems independently, move quickly from experimentation to implementation, and proactively improve engineering workflows. - Remote Collaboration: Strong written communication skills and the ability to keep colleagues informed while collaborating across US and Prague time zones. - Preferred Environment Expertise: Experience with sandboxed or ephemeral development environments using Docker, devcontainers, Kubernetes, Nix, or similar technologies. - Preferred Agent Integrations: Experience building Model Context Protocol (MCP) servers or other integrations that enable AI agents to interact securely with real systems and tools. - Preferred Observability Skills: Familiarity with OpenTelemetry, LLM tracing, agent execution telemetry, evaluation frameworks, and cost tracking. - Industry Experience: Experience in a regulated industry, such as gaming or financial technology, is an advantage. - Additional Interest: An enthusiasm for sports is a plus. ### Benefits - Annual Salary: $150,000–$190,000, with final compensation depending on experience and skill level. The stated range may be subject to change. - Potential Equity: Opportunity to earn equity in the company, subject to the applicable terms and eligibility requirements. - Healthcare Coverage: Medical, dental, and vision insurance. - Retirement Planning: Access to 401(k) or other retirement plan options. - Flexible Time Off: Flexible paid time off (PTO). - Team Events: Annual company retreats and team offsites. - Team Engagement: Regular company-wide games featuring cash prizes. - Remote Work: Full-time remote work within the United States. - Professional Impact: The opportunity to build AI-driven engineering infrastructure and contribute to products serving sports fans nationwide. - Growth Environment: Work within a fast-growing organization at the intersection of software engineering, AI automation, and fantasy sports. ###