Principal AI Engineer

Egen is a fast-growing, data-first engineering company working with advanced technology platforms including Google Cloud and Salesforce. As a Principal AI Engineer, you set the technical direction for the most complex Generative AI work in the portfolio — and you are personally hands-on building it. You own the hardest problems: agentic platforms operating at scale, foundation-model and applied ML work, and AI in regulated high-stakes environments where being wrong is expensive. You take systems from an ambiguous business problem to a robust, scalable production service, solving for real-world constraints like latency, reliability, cost, and governance. You are also the technical face of client engagements, partnering directly with client leadership to translate business strategy into AI architecture, shape solutions in pre-sales, and earn the trust that turns a project into a program. Beyond delivery, you multiply the team by elevating senior and mid-level engineers through architecture reviews, mentorship, and setting a high, teachable bar for AI-augmented engineering.

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. 10 or more years in software, AI, or ML engineering with a substantial track record of AI systems delivered to production at scale. Mastery of Python and shell scripting with fluency across the modern AI engineering stack. Deep, current expertise with Google Gemini, GPT-class, and open models such as LLaMA, including advanced prompt engineering, fine-tuning, and evaluation. Proven experience designing and productionizing agentic and multi-step reasoning systems including MCP, tool use, and orchestration. Expertise in vector databases such as Vertex AI Vector Search, pgvector, and Pinecone, and semantic search at production scale. Deep hands-on experience with Google Cloud and Vertex AI. Strong command of LangChain, LlamaIndex, or equivalent orchestration frameworks. Strong data engineering and SQL knowledge. Demonstrated technical leadership owning architecture and setting direction across engagements or teams. Senior client-facing experience translating technical complexity into business value for executive stakeholders. Experience with classic machine learning including neural networks, training, and tuning is strongly preferred.