Principal AI Engineer

As Principal AI Engineer, you will lead the technical design and development of AI agent systems and LLM-powered capabilities. You’ll operate at the intersection of applied research, systems design, and strategic customer engagement. While your day job includes architecting scalable, production-ready AI components, your role also spans into guiding enterprise customers—serving as a thought leader in pre-sales, executive briefings, and AI strategy workshops.

Key Responsibilities:

Technical Leadership

• Architect and implement advanced AI agent frameworks using LLMs (e.g. GPT, Claude, open-weight models).
• Design end-to-end systems and solutions that integrate planning, memory, retrieval, and tool usage within AI agents.
• Lead model evaluation, prompt engineering, fine-tuning, and RLHF efforts where necessary.
• Collaborate with product and engineering teams to translate AI capabilities into platform features.

Customer & Pre-Sales Engagement

• Partner with Sales, Product, and GTM to articulate AI strategy and solution architecture to enterprise clients.
• Participate in pre-sales meetings, technical deep-dives, and AI innovation workshops with C-level stakeholders.
• Translate customer needs into solution blueprints involving agents, LLMs, and AI workflows.
• Represent the company in conferences, webinars, and thought-leadership forums as an AI expert.

Requirements:

Must-Have

• PhD in Computer Science, Machine Learning, NLP, or related field.
• 7+ years of industry experience in AI/ML, with at least 2 years focused on LLMs and/or AI agents.
• Strong programming skills in Python and experience with frameworks such as LangChain, Transformers, Ray, or similar.
• Experience building production systems using LLMs (RAG, agentic workflows, orchestration, tool use, etc).
• Exceptional communication skills and a proven ability to present complex ideas to technical and non-technical audiences.
• Experience in client-facing roles or pre-sales technical advisory.

Nice-to-Have

• Experience with enterprise AI deployment (e.g., compliance, privacy, scalability).
• Publications or public talks in AI, NLP, or agentic systems.
• Contributions to open-source LLM/agentic frameworks.