Lead AI Software Engineer

About this role

Core responsibilities 

Agentic-AI architecture and production engineering 

• Define reusable architectures and patterns for stateful agents, tool calling, memory, human-in-the-loop workflows, model orchestration, RAG, and vector retrieval.

• Design, build, deploy, and operate production-grade LLM applications, agent services, integrations, retrieval layers, and tool adapters using Python and modern engineering practices.

• Lead architecture and design reviews; make pragmatic trade-offs across quality, latency, reliability, cost, maintainability, data access, and security.

• Own the transition from prototype to production, including CI/CD, observability, incident response, rollback, and continuous improvement.

Evaluation, security, and responsible AI 

• Define evaluation and regression strategies for task success, factuality, groundedness, safety, robustness, and agent behavior.

• Establish tracing, monitoring, feedback, and service-level measures for quality, availability, latency, and cost per task.

• Design controls for privacy, PII, access management, prompt injection, data leakage, unsafe tool use, auditability, and human override.

Technical leadership and collaboration 

• Partner with global AI, product, engineering, data platform, security, legal, and compliance teams to shape the roadmap and drive adoption of common AI building blocks.

• Mentor senior engineers and data scientists through design reviews, pair engineering, reference implementations, documentation, and operational playbooks.

• Evaluate models, frameworks, platforms, and third-party providers; lead technical due diligence and production-readiness assessments.

Required qualifications and experience 

• Typically, 8 + years in software engineering, machine learning engineering, AI engineering, or a related discipline; equivalent depth will be considered.

- At least 3-4 years of delivering production AI, Generative AI, machine learning, or LLM-powered systems.
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