Sr AI Engineer

About

You will be responsible for the technical vision and implementation of the AI algorithms that enable the copilot to understand, reason, and reliably answer users’ business-related questions. You will work hand-in-hand with the other tech leads and product leads to define and execute the product’s overall technical strategy.

Responsibilities

• Define and drive the AI strategy: design the technical roadmap around RAG, semantic search, language models (LLMs), agent orchestration (LangGraph, etc.), and answer quality.
• Lead and grow the team (MLEs, data scientists, data engineers): pair programming, code reviews, mentoring, hiring, and establishing best practices.
• Design and industrialize AI pipelines: ingestion, vectorization, indexing, fine-tuning, evaluation, and model monitoring.
• Ensure the robustness and scalability of AI components, working closely with the platform/backend team.
• Collaborate with Product team across squads to turn business requirements into effective technical solutions.
• Conduct continuous technology watch: stay at the forefront of RAG, LLMOps, evaluation frameworks, agents, and multimodality.

What We Expect From You

• You can switch easily between strategic and hands-on work: architect an AI system one day, and optimize a pipeline or model the next.
• You know how to balance delivery speed with technical quality.
• You can communicate clearly with both technical and non-technical stakeholders.
• You drive the adoption of best practices (testing, CI/CD, documentation, monitoring).
• You foster a strong culture of collaboration and feedback.
• You are comfortable in an agile environment (Scrum, squads, sprints, rituals).

Qualifications

• Strong understanding of LLM behavior, prompt-engineering techniques, and strategies for orchestrating multi-step AI agents
• Ability to connect AI agents with external tools, APIs, databases, and automation platforms to enable end-to-end workflow execution
• Experience designing and implementing agentic workflows, including task decomposition, tool integration, and autonomous decision-making logic.
• Proficiency in monitoring, evaluating, and optimizing agent performance, including error handling, memory management, and iterative refinement.