Senior AI Engineer – Agentic Systems

About Us

We are an AI Lab building advanced agentic systems that solve complex, real-world business problems.

Our focus is on turning sophisticated, expert-driven processes into reliable, autonomous AI systems that operate in production. We work across industries and use cases, combining LLMs, software engineering, automation, and intelligent orchestration to build systems that go far beyond simple chatbots or API integrations.

We are a small, senior engineering team with strong financial backing, real clients, and ambitious technical challenges. We move fast, make our own architectural decisions, and take full ownership of what we build.

Joining us means having a direct impact on our technical direction, engineering culture, and the systems we deliver from day one.

• Architect and build production-grade AI systems, including autonomous agents, multi-agent architectures, and complex AI-powered workflows.
• Own systems end to end, from understanding the problem and designing the architecture to implementation, deployment, monitoring, and continuous improvement.
• Design agent infrastructure, including tool execution, context and memory management, orchestration, state persistence, and error recovery.
• Make pragmatic architectural decisions, determining when to use traditional software, deterministic workflows, LLM-based components, or autonomous agents.
• Build robust backend infrastructure to support reliable, scalable, and observable AI systems operating in real-world environments.
• Develop evaluation and testing frameworks to measure system quality, reliability, accuracy, and performance, and prevent regressions.
• Work with browser automation and web-based environments, enabling agents to navigate, understand, and interact with complex websites and applications.
• Optimize systems for production constraints, balancing quality, latency, cost, scalability, and operational reliability.
• Collaborate directly with clients and technical stakeholders to translate complex business requirements into working AI solutions.

• 5+ years of professional software engineering experience, with strong backend engineering skills and experience designing and operating production systems.
• Hands-on experience developing and deploying LLM-powered applications in production, beyond experimentation and proof-of-concept projects.
• Strong proficiency in Python, including backend development, asynchronous programming, API design, and integration with external systems.
• Solid software architecture fundamentals, including distributed systems, concurrency, databases, messaging, and fault-tolerant design.
• Practical understanding of agentic systems, including tool calling, context management, orchestration, and common failure modes.
• Experience with cloud infrastructure and production engineering, including Docker, cloud services (AWS/GCP/Azure), CI/CD, logging, monitoring, and observability.
• Strong problem-solving and system-design skills, with the ability to break down ambiguous problems and independently deliver robust solutions.
• An engineering mindset focused on reliability, maintainability, and measurable outcomes, rather than simply making a demo work.

• Experience building sophisticated agentic or multi-agent systems in production.
• Experience with agent frameworks and orchestration technologies such as LangGraph, OpenAI Agents SDK, or similar tools.
• Browser automation and web technologies, including Playwright, DOM manipulation, and JavaScript-rendered applications.
• Experience with large-scale web crawling, scraping, or browser-based automation.
• Experience designing evaluation pipelines, automated benchmarks, and LLM-as-judge systems.
• Experience with multimodal AI, including vision-language models and document understanding.
• Background in machine learning, applied AI research, or model optimization.
• Experience working directly with enterprise clients or in a consulting environment.

• Real engineering challenges: Build advanced AI systems that solve meaningful problems and run in production.
• Technical ownership: Influence architecture, technology choices, and engineering practices.
• High-impact environment: Work in a small, experienced team with minimal bureaucracy.
• Exposure to cutting-edge AI: Experiment with new models, tools, and architectures while solving real engineering problems.
• Strong foundations: Financial backing, established business relationships, and a growing pipeline of challenging projects.