Senior Product Engineer - AI Native

DiliTrust; Paris; On-site; EUR 90,000 - 130,000; Full time

Job summary

DiliTrust in Paris seeks a Software/Product Engineer to join the AI-native Legal Tech platform. You will work at the intersection of AI, product, and engineering, building, improving, and scaling features across the suite.

You will own end-to-end delivery from technical design to deployment while translating model capabilities into strong user experiences. Based in France with full working rights, you will bring fluency in French and English, and a track record of delivering long-lived B2B SaaS

Qualifications

• Fluent in French and English with work authorization in France.
• Proven track record delivering B2B SaaS platforms in production.
• Strong background in scalable web architectures and modular designs.
• Experience with AI-driven product development and production-grade code.

Responsibilities

• Write specifications as durable project assets, including non-functional requirements.
• Define API contracts, module boundaries and invariants before implementation.
• Pilot coding tasks with clear briefs, bounded contexts, and traceability.
• Ensure a clear separation between producers and verifiers in development slices.
• Build gates for safe, scalable delivery with tests, contracts, and budgets.
• Design for blast radius with structured logs, traces, metrics and progressive delivery.

Skills

French & English fluency

SaaS software engineering

AI-enabled platform experience

Platform architecture

Job description

Ready to be part of the Legal Tech revolution?

Vision:

As a leading software-as-a-service (SaaS) provider,DiliTrustis 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 toleveragingAI-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 clientelecomprisinglisted companiesin major markets such as Europe, North America, and the Middle East.

Our Recognition:

DiliTrusthas 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 isDiliTrust'sproprietary 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 amodelcapability intoa greatuser 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