AI Lead Engineer

O projekcie: The AI Lead Engineer is a delivery-focused leader who owns the technical roadmap for specific AI workstreams. With 8–10 years of experience, you act as the "Technical North Star" for your squad, ensuring that high-level designs are translated into high-quality code. You are responsible for the delivery velocity, technical mentoring, and the successful transition of AI models from experimental PoCs to global production environments. - Fully remote work model - Professional training programs – including Udemy and other development plans Wymagania: - Leadership Tenure: 8–10 years in software engineering, with at least 3+ years in a technical leadership or "pod lead" role managing other engineers. - Core Tech Stack: Expert-level proficiency in Python, Google Gemini, and the LangChain/LlamaIndex ecosystem. - Advanced RAG: Deep expertise in advanced RAG techniques, such as query expansion, re-ranking, and context window management. - Infrastructure Mastery: Hands-on experience with Kubernetes (GKE) and Docker for managing scalable AI workloads in production. - GCP Expertise: Strong familiarity with Vertex AI, Model Garden, and Google Cloud’s data ecosystem (BigQuery, Cloud Storage). - Evaluation & Governance: Proven experience in implementing enterprise-grade evaluation frameworks (RAGAS, TruLens) and AI security protocols. - Strategic Integration: Mastery of REST/GraphQL API design and integrating AI into complex microservices architectures. - Problem Solving: Ability to lead troubleshooting for complex production issues related to model latency, hallucinations, or data drift. - Educational Background: Master’s degree in Computer Science, AI, or a related field (or equivalent years of experience). - Stakeholder Skills: Excellent communication skills to bridge the gap between technical teams and business stakeholders. Codzienne zadania: - Technical Delivery: Lead the end-to-end delivery of AI solutions, managing scope, timelines, and technical risks within an Agile framework. - Advanced Architecture: Design and implement complex, multi-agent architectures and workflows involving LLMs and external tool execution. - Optimization: Lead the fine-tuning of models and the optimization of indexing strategies for massive enterprise datasets. - Platform Integration: Ensure AI services are seamlessly integrated with legacy enterprise systems (CRM, ERP, CMS) via reusable microservices. - Responsible AI: Define and implement guardrails, content filtering, and data privacy compliance for all AI deployments. - Mentorship: Conduct code reviews and drive best practices in coding standards, model governance, and technical documentation.