AI Lead Engineer

The AI Lead Engineer role is at ITMAGINATION, focusing on leading AI workstreams and delivering AI solutions in a fully remote work model. The position requires extensive experience in software engineering and technical leadership, with a focus on AI technologies and integration into enterprise systems.

• 8–10 years in software engineering, with at least 3+ years in a technical leadership or "pod lead" role managing other engineers.
• Expert-level proficiency in Python, Google Gemini, and the LangChain/LlamaIndex ecosystem.
• Deep expertise in advanced RAG techniques, such as query expansion, re-ranking, and context window management.
• Hands-on experience with Kubernetes (GKE) and Docker for managing scalable AI workloads in production.
• Strong familiarity with Vertex AI, Model Garden, and Google Cloud’s data ecosystem (BigQuery, Cloud Storage).
• Proven experience in implementing enterprise-grade evaluation frameworks (RAGAS, TruLens) and AI security protocols.
• Mastery of REST/GraphQL API design and integrating AI into complex microservices architectures.
• Ability to lead troubleshooting for complex production issues related to model latency, hallucinations, or data drift.
• Master’s degree in Computer Science, AI, or a related field (or equivalent years of experience).
• Excellent communication skills to bridge the gap between technical teams and business stakeholders.

• Lead the end-to-end delivery of AI solutions, managing scope, timelines, and technical risks within an Agile framework.
• Design and implement complex, multi-agent architectures and workflows involving LLMs and external tool execution.
• Lead the fine-tuning of models and the optimization of indexing strategies for massive enterprise datasets.
• Ensure AI services are seamlessly integrated with legacy enterprise systems (CRM, ERP, CMS) via reusable microservices.
• Define and implement guardrails, content filtering, and data privacy compliance for all AI deployments.
• Conduct code reviews and drive best practices in coding standards, model governance, and technical documentation.
• Act as the technical leader and mentor for the engineering squad, ensuring high-quality code and successful transition of AI models from PoCs to production.
• Lead troubleshooting for complex production issues related to model latency, hallucinations, or data drift.