AI Tech Lead

We are looking to hire AI Tech Lead who owns the architectural backbone of our AI ecosystem.

What You Will Do

• Architectural Leadership - Define and evolve the end to end architecture for RAG pipelines, agent frameworks, and distributed inference systems. Prioritize scalability, latency, and UX aligned output determinism.
• Rigorous Evaluation - Build custom evaluation harnesses for AI agents, RAG, and LLM reasoning. Move beyond out of the box metrics with domain specific scoring, adversarial tests, and regression suites.
• Advanced AI Paradigms - Continuously integrate cutting edge methodologies (structured reasoning, tool use optimization, memory systems, multi agent collaboration).
• Technical Mentorship - Coach developers on AI security, token economics, UX patterns, and safe deployment practices. Establish coding standards and architectural guardrails.

Required Qualifications

• Proven leadership delivering production grade ML/AI systems.
• Strong foundations in vector math, LLM internals, embeddings, agent frameworks, and evaluation science.
• Knowledge of Google's GECX is a strong bonus.
• Expertise in systems level programming, distributed architecture, and performance critical code.

Skillset Requirements

• Systems Architecture - Expertise designing distributed AI systems, multi agent frameworks, and scalable RAG pipelines.
• LLM Internals - Deep understanding of transformer mechanics, attention patterns, tokenization, and inference optimization.
• Evaluation Science - Ability to design adversarial tests, regression suites, and domain specific scoring functions.
• AI Security - Knowledge of jailbreak prevention, prompt injection defense, secure tool use, and zero trust agent routing.
• Tokenomics - Ability to optimize token usage, context windows, and cost latency trade-offs.
• Distributed Systems - Experience with load balancing, sharding, concurrency, and high availability inference clusters.
• Observability - Familiarity with tracing, logging, telemetry, and agent level debugging.
• DevOps for AI - CI/CD for model updates, containerization, GPU orchestration, and rollout strategies.
• Mentorship & Leadership - Ability to enforce engineering discipline, code quality, and architectural guardrails.
• Responsible AI & Governance - Guardrail design, content-safety policy, and audit/traceability for regulated deployments.