--- title: "Senior Product Engineer - AI Native" company: "DiliTrust" company_url: "https://www.remjobs.works/companies/dilitrust" url: "https://www.remjobs.works/job/dilitrust-senior-product-engineer-ai-native-16e6936a-ca8a-4068-9bcd-3813ad413fd4" apply_url: "https://jobs.lever.co/dilitrust/d92d3355-e6c5-4949-9621-2506543e81a9" workplace: hybrid location: "Paris - La Défense" employment_type: full-time seniority: senior role: ai-machine-learning region: europe skills: ["docker", "gcp", "kubernetes", "llm", "nodejs", "postgres", "terraform", "typescript", "vue"] date_posted: 2026-09-18T13:17:41.396Z first_seen_by_remjobs: 2026-09-19T23:01:06.452Z --- # Senior Product Engineer - AI Native **DiliTrust** · Paris - La Défense Apply: https://jobs.lever.co/dilitrust/d92d3355-e6c5-4949-9621-2506543e81a9 ## About the role ##### Ready to be part of the Legal Tech revolution? **Vision: ** As a leading software-as-a-service (SaaS) provider, DiliTrust is a global company dedicated to offering an integrated suite of legal and governance products. Our vision is to digitize legal departments worldwide. With an annual growth rate of over 40% since 2020, our ambition is to become the world's leading Legal Tech company, aiming for a valuation exceeding $1 billion by 2026. **Our Impact: ** From generating General Meeting reports to leveraging AI-assisted contract lifecycle management, our teams in our 8 offices across France, the US, Mexico, MEA, Germany, Spain, Italy, and Canada are the driving force behind our global success. We proudly support 2,400 customers in 64 countries, with 80% of our clientele comprising listed companies in major markets such as Europe, North America, and the Middle East. **Our Recognition:** DiliTrust has been at the forefront of Legal Tech innovation, being the first Legal Tech with AI features since 2022. The company is renowned for providing a positive and entrepreneurial work environment. We are honored to have received the "Happy at Work" and "Tech at Work" labels every year since 2019. ##### The Role: Lini is DiliTrust's proprietary AI engine, powering Ask Lini, Risk Detector, Document Summarization, Minute Generation, and every AI capability across the suite. We are building a dedicated squad around it and are looking for a strong Software / Product Engineer, with a focus our platform architecture, to help us bring it to the next level. As a Software Engineer work at the intersection of AI, product, and engineering. You will contribute to building, improving, and scaling the features that make Lini a reliable and powerful AI layer across the entire DiliTrust suite. We are looking for an engineer who writes clean, production-ready code and is comfortable taking ownership of features end-to-end, from technical design to deployment. We also care about how you think about AI: whether you bring genuine curiosity to the product, and whether you can translate a model capability into a great user experience. --- #### Missions - Write specifications as the durable asset of the project. Executable acceptance criteria, and — crucially — the non-functional requirements that specs almost never carry: data classification, endpoint × role authorization matrix, volumetry assumptions, latency and throughput budgets. These are the requirements whose later correction is superlinear, so they get decided before generation, not after. - Freeze the contracts before any fan-out. API schemas, types, module boundaries, invariants. Agents do not negotiate an interface in the hallway: each will make a plausible and incompatible assumption, discovered at integration. - Pilot coding agents with a tight brief and a bounded context — narrow tasks, defined input and output artifacts, explicit stop conditions and budgets, full traceability of what produced each change. - Keep producer and verifier separate. You do not sign off alone on generation you piloted, and you act as independent verifier on your peers' slices — with an adversarial brief ("find what breaks against this spec"), never "confirm this looks fine". - Build the asymmetric gates that make the slice safe at volume — expensive to satisfy, cheap to check: property tests, contract tests, query-count and allocation budgets, execution-plan checks, policy-as-code, backward-compatibility proofs. Written before the implementation exists, so the agent closes the feedback loop itself without consuming human attention. - Use code reading as a calibration instrument, not as a gate. Sample deliberately, by risk zone, to measure the real defect rate of the generator-plus-gates pair and to keep the team's mental model of its own system alive. - Keep work-in-progress low. Capped PR size, thin vertical slices, trunk-based with very short branches, feature flags over long-lived branches, CI as the agent's first task rather than its last. More features in flight does not mean faster delivery when the bottleneck is verification. - Design for blast radius. Reversibility, progressive delivery, structured logs, traces, metrics and instrumentation generated as a matter of course. On many paths, detecting in five minutes beats three days of review that prevents nothing. --- #### Requirements **Being based in France with full working rights.** **Fluent in French and English.** **Experience & Seniority:** - 8+ years of professional software engineering experience, with a significant portion spent building and operating B2B SaaS platforms in production - Proven ownership of features across their full lifecycle — design, delivery, iteration, maintenance — on long-lived, multi-year products - Strong background in complex, scalable web architectures, including real modularity: you have seen where coupling stops parallel work dead **AI-Native Practice:** - Demonstrated, sustained use of coding agents in production work — not autocomplete, but delegated implementation with review and accountability - A concrete, articulated view of where generated code fails: correlated defects rather than idiosyncratic ones, uniform surface quality that destroys the usual "look here" review signals, plausibility with no author to interrogate - Comfort being accountable for code you did not author, and the discipline to refuse a change you cannot explain **Verification & Quality:** - Real fluency in property-based testing, contract testing, and deriving tests from the specification rather than from the code - Test-data strategy, including maintaining a volumetrically representative dataset as part of the verification apparatus — not a nice-to-have - Instinct for the difference between a rigorous check and a scalable one: does the cost of checking grow with the volume produced? **Security & Performance (non-negotiable on this role):** - Authorization modelling in multi-tenant systems, and why declarative, centrally enforced authorization beats reviewing each endpoint. Generated code reliably checks who you are and regularly forgets whether you are allowed - Awareness of the attack surface specific to an agent-assisted pipeline: prompt injection through ticket descriptions, code comments and dependency READMEs; supply-chain risk on hallucinated package names; least privilege for non-human identities; and the most frequent risk of all — production data or proprietary code leaking into the development loop - Performance as a measured number rather than a code-reading opinion: query budgets per HTTP request, N+1, unbounded result sets, missing indexes on new query paths, network calls in loops, execution-plan review. These are invisible at test-data scale and expensive in production **Product & Team Collaboration:** - Excellent written communication — specification writing is now a core engineering skill on this role, not documentation overhead - Extensive experience working directly with product managers, designers and stakeholders in a product-oriented setup - Strong sense of ownership and sound judgment on risk: what to build, what not to build, what residual risk is acceptable and why **Education:** - Master's degree in Engineering or equivalent practical experience in senior SaaS environments --- #### What this role is not We would rather say it up front: - It is not a prompt-engineering role, and it is not an ML/LLM research role. - It is not a role where green tests are sufficient evidence. On security and performance, "the spec is met and the tests pass" is not a weak signal — it is a null one. - It is not a ticket queue. The profile that shrinks in an AI-native team is the one whose value was implementing assigned tickets between two boundaries. --- #### Our Tech Stack - Backend: Node.js / TypeScript - Frontend: Vue.js 3 - Database: PostgreSQL, MariaDB - DevOps: Docker, Kubernetes, Terraform - Cloud: GCP **Engineering apparatus you will use and help build:** coding agents orchestrated deterministically in CI, aspect-scoped verification agents (authorization, performance, spec conformance, dependencies), policy-as-code, property-based and contract testing, volumetric test datasets, load and endurance gates on critical paths, feature flags, progressive delivery and end-to-end tracing. --- #### What we offer - Join a fast-growing company in a friendly, international environment — engineering primarily in Paris, with further engineering presence in Berlin, Montreal and Wilmington (DE), and offices across France, Italy, Spain, Germany, Canada, the USA, Mexico and Dubai; - Our "Remote Policy" guarantees that you can find the right balance between "Onsite" and "Remote"; - Last but not least, all the day-to-day benefits of the CSE, luncheon vouchers, profit sharing bonuses, etc... --- #### Recruitment process - Interview with a TA team member (30/45 mins) - Interview with the Engineering Manager (1h) - Technical interview (1h30) — two parts: turning an ambiguous requirement into a specification with executable acceptance criteria, then an adversarial review of an agent-generated diff against that specification - Interview with the CTO (45 mins) --- Source: DiliTrust's own career page, read by RemJobs. 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