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A visionary systems-level architect who treats AI as an engineering discipline, not an experimentation playground. This role sets the technical north star for generative AI programs, ensuring every agent, pipeline, and evaluation framework is built with rigor, determinism, and long-term maintainability.

About the Role

The AI Technical Lead owns the architectural backbone of AI ecosystem. You will steer the design of RAG systems, agentic orchestrations, evaluation harnesses, and deployment patterns that scale across enterprise workloads. You will mentor developers, enforce engineering discipline, and ensure the AI stack remains transparent, debuggable, and future-proof.

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.