Software/MLOps Engineer (Python, AWS)
Actively Hiring
Full-time Posted 5 months ago
Responsibilities
- check_circle Design and build APIs and pub/sub event streams to support real-time machine learning inference and automated agentic processes.
- check_circle Play a role in the development and maintenance of both online and offline feature stores for machine learning.
- check_circle Gain familiarity with the property casualty insurance sector, including key policyholder and product attributes, to help enhance model effectiveness.
- check_circle Implement industry-standard MLOps and LLMOps techniques to monitor ML models, feature sets, and agentic systems for performance degradation and data drift.
- check_circle Support the ongoing development of our core MLOps platform, as well as the codebase and infrastructure for serverless AI applications.
- check_circle Validate the performance of machine learning models through rigorous training and testing methodologies.
- check_circle Collaborate with Data Science teams to engineer new features, construct transformation pipelines, integrate custom loss functions, and experiment with novel inference strategies such as chaining and shadow deployments.
- check_circle Create and scale new agentic AI automations, guiding them from initial proof-of-concept through to full production deployment.
- check_circle Construct evaluation frameworks designed to rigorously test AI applications, covering not only standard workflows but also the complex, real-world scenarios common to the car insurance domain.
- check_circle Utilize the Python data ecosystem to execute machine learning projects and initiatives.
- check_circle Take part in the team's weekly on-call rotation, addressing alerts promptly to maintain high service availability for both customers and internal stakeholders.
Basic qualifications
- Experience with Python (production-quality code)
- Experience with Python data science and machine learning libraries, including scikit-learn, pandas, numpy and related libraries.
- Hands-on experience deploying and operating ML models in production.
- Hands-on AWS experience (Lambda, Step Functions, DynamoDB, IAM, containers)
- Experience with Kafka or other event-driven systems
- Experience deploying ML models to production
- Git / CI/CD experience
Preferred qualifications
- Experience with MLOps platforms and automation tools
- Real-time data pipelines
- Experience with AI chatbots or retrieval-augmented generation (RAG) systems
Tags & Focus Areas
Fulltime Ai Machine Learning Mlops Generative Ai
About Sphere
Puesto: Oficial de CumplimientoUbicación: MéxicoEmpresa: SphereDescripción de la empresa: Sphere es una plataforma líder en soluciones de pagos globales. Ofrecemos servicios innovadores tanto para empresas como para individuos, garantizando cumplimiento normativo y seguridad en todas nuestras operaciones.Responsabilidades: Desarrollar, implementar y supervisar políticas y procedimientos de cumplimiento para garantizar la conformidad con las regulaciones financieras mexicanas, especialmente en...