Fetcherr builds responsible AI that transforms market complexity into measurable profit growth.

At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets.

Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul.

We are seeking a hands-on technical leader to architect and lead the development of production LLM-based applications. This role will lead a cross-functional team of software engineers and data scientists and will own architecture, delivery, evaluation, observability, and production readiness.

The ideal candidate is a proven software leader who has delivered complex production-grade systems and can align engineering, data science, product, and platform work into one cohesive execution model. The role requires strong software architecture, applied AI judgment, disciplined execution, and people leadership, with a focus on turning ambiguous product goals and exploratory AI work into reliable production software.

Responsibilities:

• Lead a cross-functional team of engineers and data scientists developing LLM-based applications.
• Translate product and business goals into technical roadmaps, milestones, and deliverable plans.
• Own end-to-end delivery from architecture and experimentation through deployment, monitoring, evaluation, and continuous improvement.
• Design LLM workflows using RAG, tool calling, structured outputs, agents, deterministic logic, and human-in-the-loop patterns where appropriate.
• Establish evaluation-driven development practices for LLM features.
• Define quality metrics, regression tests, golden datasets, production feedback loops, and observability standards.
• Ensure systems meet production standards for scalability, performance, reliability, security, and maintainability.
• Provide technical guidance, architectural oversight, mentorship, and career development for team members.
• Manage priorities, scope, risks, and trade-offs across multiple streams of work.
• Partner with product, design, DevOps, data engineering, security, and other technical leaders.
• Drive continuous improvement in delivery quality, engineering practices, and team effectiveness.



• 8+ years of software engineering experience, including 3+ years in technical leadership, team leadership, or architecture roles.
• Solid background in ML & LLM foundations and development methodologies
• Solid background in “classical” software engineering
• Proven track record leading cross-functional teams and delivering complex production-grade systems.
• Strong understanding of modern software architecture, distributed systems, APIs, cloud-native systems, and scalable application design.
• Fluency in fullstack technologies: python, typescript, javascript, SQL, NoSQL
• Hands-on experience with LLM-based, AI-assisted, ML-powered, or data-intensive applications.
• Practical understanding of RAG, tool calling, structured outputs, prompt/version management, workflow orchestration, agents, and LLM evaluation.
• Advanced usage of LLM based coding assistance technologies: claude code, codex, etc.
• Experienced and pragmatic code reviewer.
• Strong ability to plan, prioritize, and execute across multiple streams of work.
• Strong judgment around architecture, reliability, latency, cost, security, data access, and user trust.
• Excellent leadership, communication, mentoring, and decision-making skills.
• Comfortable working in a fast-moving environment with ambiguity and evolving requirements.

Bonus Points:

• Background in NLP, search, data-intensive systems, or applied data science.
• Experience with vector search, hybrid search, embeddings, reranking, document processing, and retrieval evaluation.
• Experience with LLM observability, evaluation harnesses, model gateways, MLOps, or ML platform practices.
• Experience with enterprise LLM applications, data assistants, workflow copilots, text-to-SQL, BI systems, or semantic layers.
• Experience with CI/CD, containerized services, k8s, data pipelines, production monitoring, GCP.