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.
