Agentic Commerce AI Engineering Lead

Azoma is a hypergrowth startup defining the agentic commerce category. Our revenue has grown tenfold over the past year, and we now work with 20 of the world’s 50 largest CPG companies, alongside other enterprise customers.

We’re building the merchant-side infrastructure for the era of personal shopping agents - transforming how brands and retailers work with AI agents that discover, evaluate and purchase products on behalf of consumers.

Our team combines advanced AI, software engineering and deep commerce expertise. Joining Azoma means taking hands-on ownership as we scale, shaping products used by global brands and helping define how commerce works in the age of AI.

Own the research and engineering that make Azoma’s agentic commerce technology distinctive. You’ll develop how we model shopping intent, understand products, evaluate AI answers and automate useful commerce actions. We need a technically ambitious builder who can turn emerging research into reliable capabilities, demonstrate why they improve results and make that work compelling to both enterprise customers and our engineering team.

What you will do

• Translate commerce problems into research questions and measurable technical objectives; investigate new models and methods, reproduce promising results and decide where original development creates an advantage.
• Personally build AI capabilities across shopper and product understanding, retrieval and ranking, model adaptation and agent workflows, choosing techniques according to the problem and available data.
• Take successful experiments into production with engineers: design backend services, tool interfaces, monitoring and recovery, and improve quality alongside latency, reliability and operating cost.
• Lead technical customer discussions and internal reviews, using clear explanations and working demonstrations to connect architecture and scientific evidence to customer outcomes and product decisions.

Skills and experience

• You have personally taken substantial ML or AI work from a research hypothesis into a maintained production system, and can explain the improvement and your implementation contribution.
• Strong Python, machine-learning foundations and backend engineering; experience with LLMs, retrieval, ranking, recommendation or agent systems, supported by depth beyond assembling model API calls.
• Sound experimental and statistical judgment: you can create valid comparisons, diagnose failure modes and recognize when an apparent gain will not generalize to real customer workloads.
• Ability to read research critically and implement useful advances, balancing scientific ambition with data quality, engineering complexity and the commercial importance of the problem.
• Excellent communication with technical and business audiences. You can inspire confidence, explain difficult ideas simply and answer challenging questions about your own systems and evidence.