At DigitalGenius (DG), our mission is to make online shopping a seamless experience. We believe we can achieve this using conversational AI and deep integrations.

While most chatbots only cause frustration, we have built an e-commerce chatbot that actually works. It resolves customer issues end-to-end, detects carrier problems, performs investigations, issues return labels, refunds, and replacements — and does all of this proactively, often before the customer even notices the issue.

Some of the customers who have joined us on this mission: On, Holland & Barrett, Cozey, Caraway, MyTheresa, Clarins, Air Up, Universal Music Group, Huda Beauty.

We are a committed team of like-minded people who believe in the product we are building and the vision behind it. Each of us operates in our own bubble of autonomy and responsibility, but the magic happens when we collaborate and make all the pieces fit together. DG is for the passionate, the curious, and the overachievers. We thrive by exceeding expectations — not because we have to, but because we care.

About the role

A Solutions Engineer at DigitalGenius works in post-sales, partnering closely with Product and Customer Success Managers (CSMs) to design, implement, and optimize AI-driven customer service solutions for our clients. You are the technical owner of customer implementations, ensuring every solution is scalable, secure, high-performance, and aligned with both business goals and our platform's capabilities.

You'll spend your time turning messy real-world e-commerce problems into reliable AI-powered automations — designing flows, wiring up integrations, writing Python where the platform stops, prompting and evaluating LLMs, and continuously raising the bar on what our AI agents can resolve autonomously.

Key Responsibilities

Solution Design & Implementation

Translate business requirements into scalable technical solutions on the DigitalGenius platform
Configure workflows, AI agents, integrations, and automation logic for complex e-commerce use cases
Design and implement APIs and third-party integrations (OMS, carriers, Helpdesk, etc.)
Prompt, tune, and evaluate LLM-powered components — classifications, extractions, guardrails, and generative replies

Technical Ownership (Post-Sales)

Lead new use case implementation projects
Ensure timely, high-quality launches with measurable automation impact
Troubleshoot complex technical challenges across systems, data, and AI behavior

Customer Collaboration

Partner with CSMs to align technical delivery with customer outcomes and ROI
Advise customers on best practices, scalability, AI strategy, and optimization
Support strategic expansions, new channels, and advanced use cases

Product Partnership

Provide structured feedback to Product based on what you see in real implementations
Identify feature and AI capability gaps and contribute to roadmap discussions
Participate in beta testing, model evaluations, and product validation initiatives

Optimization & Innovation

Continuously improve automation performance, resolution rates, and AI accuracy
Proactively identify opportunities for process improvements — both for customers and internally
Contribute reusable assets, documentation, prompts, evaluation sets, and internal best practices
Use AI to automate your own work: build internal tools, scripts, and agents that make the SE team faster and better

Requirements

Required

AI-first mindset. You're genuinely excited about LLMs and applied AI. You actively use AI tools in your day-to-day work, you push to automate the repetitive parts of your own job, and you believe most manual work in our space will eventually be done by agents.
Automation / workflow mindset. You can translate business requirements into reliable flows — including edge cases, data mapping, retries, and clear decision logic.
Strong API + integration experience (building and debugging). Comfortable with Postman, JSON, webhooks, auth (tokens, headers, OAuth), pagination, error handling, and troubleshooting unexpected API behavior.
Python. Hands-on experience writing and shipping Python used by others — integrations, automation scripts, services, or internal tools. You can explain your design choices and how you tested and debugged your code.
Strong communication. You can explain technical concepts clearly to non-technical stakeholders, write cle