P
Senior AI Engineer
В офисеПолная занятостьAI EngineeringEnterprise Software
Build and operate AI-integrated data systems, backend services, and production capabilities for an AI-native predictive analytics platform. Own solutions from architecture and implementation through deployment, monitoring, cost management, and business impact.
Обязанности
- Design, build, and own scalable data and ML pipelines, backend services, and AI-powered production capabilities.
- Integrate AI and ML components as runtime dependencies in production decision-making systems.
- Ship software in small, safely mergeable increments using feature flags, canary releases, and rollback strategies.
- Use and evaluate AI-assisted development tools for code generation, testing, and architecture prototyping.
- Own solutions through design, deployment, operational monitoring, cost efficiency, and business impact measurement.
- Maintain CI/CD pipeline health and observability.
- Make, document, and own pragmatic architectural decisions using lightweight ADRs.
- Collaborate with product, design, infrastructure, and go-to-market teams to translate customer needs into technical solutions.
Требования
- At least 5 years of experience building and shipping production-grade backend and data systems in distributed cloud environments.
- Production experience integrating AI, machine learning, LLM, or agent-based components into live workflows.
- Active use of AI-assisted development tools such as Copilot, Cursor, or equivalent.
- Strong backend development experience with Java and Spring Boot, Python, and/or Go.
- Experience with relational and non-relational databases, data modeling, and query optimization.
- Expertise in automated testing, CI/CD, and observability.
- Ability to decompose complex work into incremental deliveries and ship safely on a frequent basis.
- Ability to make pragmatic architectural decisions and balance reliability, cost, and delivery speed.
Будет плюсом
- Production MLOps experience, including model serving, monitoring, or retraining pipelines.
- Experience with distributed data technologies such as Parquet, Athena, or similar query engines.
- Experience documenting autonomous architectural decisions through ADRs or equivalent records.