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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Software Engineer, AI SaaS Platform (Full-Stack & ML) - **Company:** Arcascience - **Location:** Paris, France - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Software as a Service, Cloud Computing, Continuous Integration, DevOps, Elasticsearch, Python (Programming Language), PostgreSQL, Natural Language Processing, Named Entity Recognition, Node.Js, NoSQL, Ansible, Search Technologies, Software Engineering, Test-Driven Development (TDD), ReactJS, System Availability, Large Language Models, Apache Spark, Git, Fastapi, Vue.js, Containerization, AngularJS, Gitlab-ci, Kubernetes, Information Technology, Machine Learning Operations, Front End Software Development, Restful APIs, Spacy, Terraform, Data Pipelines, AWS EKS, Docker - **Published:** June 29, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=e23dfbbcb600b761 ## About the Role Do you have experience in Terraform?, Do you have a Master's degree?, We want a strong full-stack SaaS engineer with a real ML track record, not five specialists in one. Full-stack depth and hands-on NLP are the non-negotiable core; the rest is learnable on the job. * Degree in Computer Science / Engineering or equivalent, with 8+ years in software engineering including high-traffic SaaS platforms. * Strong full-stack experience: a modern front-end (React preferred, Vue or Angular accepted) plus Node.js and Python backends. * 3+ years of professional Python. * A genuine AI/ML track record: you have personally trained and shipped NLP models (NER, extraction, classification, standardization), not just consumed APIs. * Hands-on MLOps: experiment tracking and LLM observability (MLflow, Langfuse, LiteLLM, or equivalents). * REST, NoSQL, DevOps fundamentals, cloud (AWS, GCP, or Scaleway), and containerization (Docker, Kubernetes). * Git, plus a real understanding of accessibility and security/compliance. * Product orientation: a demonstrated ability to ship the right thing fast. * Excellent English (French a plus) and proven team leadership. Strong Pluses * Our data stack: FastAPI, SpaCy, Spark NLP, Airflow, S3, PostgreSQL, Elasticsearch, Qdrant. * IaC and CI/CD: GitLab CI, Terraform, Ansible, AWS EKS. * Regulated/high-compliance experience (medical, pharma, fintech), Agile and TDD, vector search/RAG and LLM extraction pipelines. ## Description We are hiring one Tech Lead to own our AI SaaS platform end to end, from a high-traffic, secure product surface down to the NLP models and data pipelines that power it. You will lead a cross-functional team (fewer than 10 engineers and ML/Data engineers), set technical direction across full-stack and ML, and stay close enough to the code to ship. You will work daily with scientists, biologists, product, and regulatory. This is not two jobs bolted together: the best AI products come from one person who can reason from the user-facing feature down to the model that powers it. What You'll Own Platform & Product (Full-Stack SaaS) * Ship scalable, maintainable features across the stack (React, Node.js, Python). * Architect for high traffic, speed, and scalability, with security and compliance built in by design. * Stay product-oriented: ship valuable features fast to hit short time-to-market. AI / ML & Data * Train and improve NLP models: classification, named entity recognition (NER), relation and text extraction, entity linking, standardization, aggregation. * Own the full NLP/ML lifecycle: training, evaluation, deployment, monitoring. * Run MLOps with tools like MLflow, Langfuse, and LiteLLM (experiment tracking, observability, cost control, validation automation), and integrate LLM APIs into production. Pipelines & Reliability (SRE / DevOps) * Own the data pipelines behind delivery, handling hundreds of daily workflows over millions of biomedical documents. * Ensure high availability and performance in production, and drive data quality and compliance to strict medical standards. Leadership * Manage, mentor, and grow the team; deliver solutions aligned with business goals. * Communicate clearly with technical and non-technical stakeholders (CTO, product, regulatory, customers). What Success Looks Like In your first year: platform and pipelines mapped and shipping faster, a clean MLOps loop in place (tracking, observability, evaluation), measurable gains in model quality and delivery speed, a grown team, and benefit-risk analysis moving toward real-time. 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