AI Solutions Engineer

Why Omnilex?

At Omnilex, we’re on a mission to transform the way lawyers work. Our AI-native platform lets legal professionals enhance their productivity in legal research and automate workflows. We collaborate closely with our clients and iterate at a market-leading pace. In a year, we have gone from an early MVP to a product used daily by thousands of legal professionals at our clients in Switzerland, Germany and Liechtenstein - and are now scaling rapidly across Europe.

We already stand out with our strong data engineering, including our combination of external data, customer-internal data and our own innovative AI-first legal commentaries.

You’ll be joining a young, passionate, and dynamic team of 14, with roots at ETH Zurich.

Your role

Do you enjoy taking a strong AI product and making it work beautifully inside a customer’s real-world environment; messy data, unique workflows, strict permissions, and high expectations? Are you hands-on, pragmatic, and happiest when you can ship an improvement that a specific legal team immediately feels?

You’re comfortable being the “technical bridge” between customers, legal experts, and the core product team: you diagnose issues, propose solutions, implement them, and leave behind clean playbooks so the next deployment gets easier.

What you'll do

As an AI Solutions Engineer, you will focus on deploying, tailoring, and operationalizing Omnilex’s legal search + LLM workflows for customers; while feeding the best learnings back into the product.

Customer deployments & adaptations (core of the role)

• Own technical onboarding for new customers: data ingestion, indexing, metadata mapping (jurisdiction, authority, recency), and validation.
• Configure and adapt retrieval + reranking pipelines to customer needs (practice area focus, doc structure, internal taxonomies, “what good looks like”).
• Implement customer-specific workflows: templates, filters, jurisdiction defaults, citation behavior, permission-aware retrieval, and custom result layouts.

LLM workflows that are production-safe

• Tailor prompting / context engineering to customer requirements (traceability, citation style, explanation depth, fallback behavior).
• Implement safeguards: provenance, source grounding, “no-citation → no-claim” behaviors, and “confidence/uncertainty” patterns aligned with legal risk.

Evaluation & iteration in the field

• Build lightweight customer-specific eval sets (gold questions, acceptance criteria, “must-not-fail” cases).
• Run fast error analyses and ship fixes: query understanding tweaks, reranker tuning, chunking strategy, dedupe suppression, caching, and retrieval routing.

Performance, cost, reliability

• Keep latency and costs under control with caching, batching, early exit, and sensible fallbacks.
• Monitor quality + usage signals; turn customer feedback into concrete improvements and measurable acceptance checks.

Collaboration & knowledge transfer

• Work closely with Customer Success + legal experts to translate pain points into system changes.
• Document integrations and “deployment recipes” so solutions become reusable product capabilities over time.

What you'll bring

Minimum qualifications

• Strong hands-on experience building or adapting search/retrieval systems in production (hybrid retrieval, reranking, query understanding, indexing).
• Proven experience taking LLM workflows from prototype to reliable production use.
• Proficiency in TypeScript/Node.js (our core stack).
• Experience with one or more of: Azure AI Search, pgvector/PostgreSQL, OpenSearch/Elasticsearch (or similar).
• Practical engineering instincts: debugging, performance tuning, careful handling of edge cases, and clear operational thinking.
• Strong communication skills and comfort working directly with customers (technical deep-dives, explaining trade-offs, writing playbooks).
• Proficiency in English; full-time availability.
• Hybrid presence: on-site in Zurich at least two days per week.

Preferred qualifications

• German proficiency (many sources and customer interactions are German-speaking).
• Experience integrating customer data sources / document pipelines (connectors, ETL, access controls).
• Experience with pragmatic eval pipelines (human-in-the-loop labeling, inter-annotator agreement, lightweight dashboards).
• Familiarity with sparse + dense retrieval methods (BM25 variants).
• Experience operating services (Docker is a plus).
• Familiarity with our stack: Azure / NestJS / Next.js.
• Knowledge of Swiss / German / US legal systems is a plus.

Benefits

• Customer-visible impact: your work directly determines whether customers trust the product in daily legal workflows.
• Autonomy & ownership: you’ll own deployments end-to-end and shape repeatable “solution patterns.”
• Fast learning loop: see real-world failure modes early; help steer product priorities with evidence.
• Compensation: CHF 8’000-12’000 per month + ESOP, depending on experience and skills.

We’re excited to hear from candidates who are passionate about making legal search work beautifully for our users.