Forward Deployed AI Engineer

Description

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.

You’ll be joining a young, passionate, and dynamic team of 15, with roots at ETH Zurich.

As a Forward Deployed AI Engineer, your mission is to bring Omnilex into customer environments and make it work exceptionally well—then turn what you learn into reusable product capabilities.

Responsibilities

CUSTOMER ROLLOUTS & CUSTOMIZATION (THE HEART OF THE JOB)

• Lead technical onboarding for new customers: ingest documents, build indexes, map metadata (jurisdiction, authority, recency), and run validation checks
• Tune retrieval and reranking behavior to match customer expectations (practice area focus, internal taxonomies, document patterns, relevance definitions)
• Deliver customer-specific UX and workflow adaptations: templates, default filters, jurisdiction presets, citation formatting, permission-aware retrieval, and customized result views

PRODUCTION-GRADE LLM WORKFLOWS

• Adjust prompting and context strategies to meet strict requirements (grounding, traceability, citation style, explanation depth, fallback behavior)
• Build and enforce guardrails: provenance tracking, source-grounded generation, “no source → no statement” rules, and risk-aware uncertainty patterns suitable for legal contexts

FIELD ITERATION & QUALITY LOOPS

• Create small but high-signal evaluation sets per customer (gold questions, acceptance criteria, “cannot fail” scenarios)
• Perform fast failure analysis and ship improvements: chunking changes, deduping, reranker adjustments, query interpretation tweaks, caching, and routing strategies

LATENCY, COST, AND OPERATIONAL RELIABILITY

• Keep response times and usage costs sane through batching, caching, early exits, and practical fallback paths
• Track quality signals and usage patterns; convert feedback into measurable fixes and clear acceptance tests

CROSS-TEAM EXECUTION & KNOWLEDGE CAPTURE

• Work closely with Customer Success and legal experts to convert pain into engineering work
• Write deployment playbooks and integration “recipes” so customer solutions become repeatable patterns over time

Qualifications

MUST-HAVES

• Strong practical experience building or adapting search/retrieval systems in production (hybrid retrieval, reranking, indexing, query understanding)
• Experience taking LLM features from prototype to stable, real-world usage
• Solid TypeScript/Node.js skills (our core stack)
• Hands-on experience with at least one of: Azure AI Search, pgvector/PostgreSQL, OpenSearch/Elasticsearch (or comparable systems)
• Strong engineering judgment: debugging skills, performance tuning, careful edge-case handling, and operational thinking
• Comfortable working directly with customers: deep technical sessions, trade-off explanations, and clear written documentation
• Fluent English; available full-time.
• Hybrid setup: at least two days per week on-site in Zurich.

NICE-TO-HAVES

• German proficiency (many sources and stakeholder conversations are German-speaking)
• Experience integrating customer document sources and pipelines (connectors, ETL, access controls)
• Experience with lightweight evaluation processes (human labeling loops, basic agreement checks, simple dashboards)
• Familiarity with sparse + dense retrieval approaches (BM25 variants included)
• Experience running and operating services (Docker a plus)
• Familiarity with Azure / NestJS / Next.js
• Exposure to Swiss / German / US legal systems

Benefits

• Tangible customer impact: your work directly affects daily trust and adoption inside legal teams
• High ownership: you run deployments end-to-end and help define reusable solution patterns
• Fast feedback loops: you’ll see real failure modes early and influence product direction with evidence
• Compensation: CHF 8’000–12’000 per month + ESOP, depending on experience and skills