Senior Software Engineer, Pipeline Team

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Foundation AI

Site WebLinkedInTous les Emplois

51 - 200 employés

Fondée en 2019

🤖 Intelligence artificielle

💳 Fintech

💰 Seed Round en 2019-04

Artificial Intelligence • Legal • Fintech

Foundation AI est une entreprise technologique innovante spécialisée dans l'automatisation du traitement des documents entrants pour le secteur du contentieux et des sinistres. En utilisant une intelligence artificielle avancée, Foundation AI rationalise la classification, l'indexation et l'acheminement des documents, améliorant ainsi considérablement l'efficacité opérationnelle des cabinets d'avocats et des départements de gestion des sinistres. Cela permet aux organisations de réduire les délais et les coûts de traitement, permettant ainsi aux équipes de se concentrer sur des tâches à plus forte valeur ajoutée et d'améliorer leur productivité globale.

Description

• Design, build, and maintain end-to-end ML pipelines covering data ingestion, preprocessing, model training, evaluation, and serving. • Own model versioning, data versioning, and prompt versioning across environments. • Build and operate side-by-side deployment infrastructure and A/B testing frameworks to evaluate model variants in production with rigorous statistical guardrails. • Implement drift detection, data quality monitoring, and alerting across the pipeline stack. • Extend CI/CD practices to the ML lifecycle—automating training triggers, evaluation gates, and deployment workflows integrated with the broader engineering delivery pipeline. • Design and implement robust, high-performance, and secure ML infrastructure. • Provide mentorship and guidance to junior engineers, foster a culture of knowledge-sharing, and influence ML engineering best practices at the team and organizational level. • Ensure code quality through peer reviews, unit testing, and adherence to coding standards across pipeline and platform code. • Work closely with ML scientists, product managers, and infrastructure teams to translate model development needs into reliable production systems. • Ensure pipelines and model artifacts follow best security practices and industry compliance standards relevant to legal document processing. • Maintain clear technical documentation for pipelines, model registry conventions, and operational runbooks.

🎯 Exigences

• 5+ years in software engineering, with at least 2–3 years in ML engineering, MLOps, or AI platform roles. • Hands-on experience with model versioning, data versioning, prompt versioning, experiment tracking, and deployment automation in production environments. • Proficiency with workflow orchestration (Apache Airflow or equivalent), experiment tracking (MLflow or equivalent), and cloud-based model hosting (AWS Bedrock or equivalent). • Experience designing and operating side-by-side deployments, shadow mode evaluation, canary releases, and automated rollback strategies for ML models. • Familiarity with model drift detection, data quality monitoring, and pipeline alerting; experience defining and tracking ML-specific SLOs. • Experience with AWS services (S3, ECS/EKS, Lambda, Step Functions, or equivalents); comfort operating in a cloud-native environment. • Proficient in Python; writes scalable, maintainable, and secure code. • Experience with SQL and familiarity with data engineering patterns is a plus. • Experience extending CI/CD principles to ML workflows, including automated training pipelines, evaluation gates, and model promotion flows. • Designs modular, high-performance systems; able to drive technical decisions and articulate trade-offs clearly. • Implements automated testing for pipeline components; values reproducibility and reliability in ML systems. • Tackles ambiguous, complex challenges; evaluates trade-offs across performance, reliability, and development velocity.

🏖️ Avantages

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Professional development opportunities