Senior AI Engineer

Apilex is an AI-native legal operating system that manages lawyers' entire legal workflows end to end, powered by AI systems purpose-built for each jurisdiction's legal framework. We bring legal research, drafting of pleadings and contracts, document analysis, and case preparation together in a single experience. We currently operate in France, Turkey, and Germany, and we aim to extend the growth we've achieved in our current markets to Brazil, Spain, and Italy.

We achieved this growth entirely bootstrapped, without raising any external investment, making us one of the fastest go-to-market success stories among Legal AI startups in the world. Since our founding, we've scaled our team to 120+ people, backed by a proprietary database of millions of court decisions and legal documents that turns days of legal research into seconds.

What sets us apart?

We don't use off-the-shelf AI. We build domain-specific, closed-circuit AI models with multi-layered verification systems that eliminate hallucinations and deliver answers backed by real citations and legal reasoning. Every feature we ship changes how lawyers work.

As a Senior AI Engineer, you will design, build, and operate AI systems used across real legal workflows. You will take ownership of technically challenging projects, contribute to architecture decisions, and work closely with researchers, engineers, and legal domain experts to turn new methods into reliable production systems.

Key Responsibilities

• Design, build, and improve AI systems for complex legal reasoning and long-horizon task execution.
• Develop agentic systems that can plan, use tools, manage state, recover from errors, and complete end-to-end legal workflows reliably.
• Build graph-based multi-agent orchestration systems that represent workflows, dependencies, state transitions, and coordination between specialized agents.
• Design and implement graph engineering solutions across GraphRAG, personalized knowledge bases, and multi-agent orchestration.
• Build GraphRAG systems that connect structured and unstructured legal knowledge to improve retrieval, reasoning, and source-grounded outputs.
• Develop personalized knowledge bases that incorporate user, organization, matter, and jurisdiction-specific context while respecting data isolation and access controls.
• Work on graph schemas, ontologies, entity and relationship extraction, entity resolution, graph storage, and retrieval pipelines for legal knowledge.
• Optimize agent harnesses, including tool interfaces, context management, memory, orchestration, and execution environments.
• Contribute to model post-training for advanced legal reasoning, including supervised fine-tuning, preference optimization, and reinforcement learning approaches where appropriate.
• Develop reward models, verifiers, and automated verification systems for evaluating legal reasoning, citations, intermediate steps, and final outputs.
• Build scalable pipelines for producing, filtering, and validating high-quality synthetic and human-generated training data.
• Create evaluation methodologies, domain-expert benchmarks, and regression suites that measure capability, reliability, and behavior across jurisdictions.
• Improve experimentation and observability systems so that model and agent changes are measurable, reproducible, and safe to deploy.
• Make sound engineering decisions across model selection, inference infrastructure, latency, cost, reliability, and performance.
• Participate in architecture reviews, share knowledge with the team, and support other engineers through technical collaboration and code reviews.

What We’re Looking For

• 5+ years of engineering experience building and operating AI or machine learning systems in production.
• Strong Python engineering skills and experience designing reliable, scalable systems.
• Hands-on experience building agentic systems that use tools, maintain state, and execute multi-step tasks.
• Hands-on experience building production-grade RAG systems.
• Experience working with graph-based systems, such as knowledge graphs, GraphRAG pipelines, graph databases, or graph-based agent workflows.
• Strong understanding of retrieval systems, including chunking, embeddings, reranking, structured retrieval, entity resolution, and graph traversal.
• Strong understanding of evaluation design, including benchmark construction, error analysis, model comparisons, and regression testing.
• Experience building or working with verifiers, reward models, judge systems, or other automated methods for assessing model outputs.
• Familiarity with training data pipelines, synthetic data generation, data filtering, and quality assurance.
• Practical understanding of modern language model infrastructure, including inference, serving, experimentation, and observability.
• Ability to work effectively across open-ended research problems and production engineering constraints.
• Strong technical judgment and a practical approach to solving problems where established best practices may not yet exist.

Nice to Have

• Experience with long-horizon agents, reinforcement learning for language models, or inference-time scaling.
• Experience with model post-training methods such as supervised fine-tuning, preference optimization, reinforcement learning, or distillation.
• Experience designing knowledge graph schemas, ontologies, entity resolution systems, or graph retrieval pipelines.
• Experience with graph databases such as Neo4j, Amazon Neptune, Memgraph, or similar technologies.
• Experience building personalized or organization-specific knowledge systems with permission-aware retrieval and secure data isolation.
• Experience evaluating reasoning systems in high-stakes or expert domains.
• Experience working with legal documents, citation systems, or domain-expert evaluation.
• Research contributions, open-source work, or publications related to language models, agents, evaluation, knowledge graphs, interpretability, or AI reliability.

Why join us?

Whatever your role, your work here will be visible and meaningful. We're a team that has grown without outside investment and achieved one of the fastest go-to-market successes in the space and we're looking for ambitious people who want to build the future of legal technology on a global stage.

This is Apilex. AI for Legal. Join us.