MLOps Senior [Risk Team]
我们正在寻找高级 MLOps 工程师加入我们的风险团队。风险团隊负责自动化 IT 解决方案的风险管理流程。在这个过程中，我们通过指定 SLA 实时解决模型创建和推断生产任务，集成大量外部服务并仅基于数据做出决策。我们的数据科学团队开发了包括神经网络基础方法在内的分数模型，我们需要工业化整个 ML 生命周期。我们正在寻求一个 MLOps 工程师来从零开始构建 ML 基础设施自动培训管道到提供 DWH 研究和实时生产推断的统一功能平台。挑战等待你 · 对功能商店/功能登记解决方案进行评估 （Feast, Tecton 或定制）,做出建议，设计架构和功能生命周期过程 （实验 →稳定 →生产）.由工程团队和平台领导实施 · 设计，建造和拥有 ML 培训和部署管道：实验跟踪，模型登记，模型 CI/CD,包装和交付到生产。选择平台 （MLflow 或替代品）,建立版本化和验证标准，并不断发展基础设施 · 选择工具和设置与 DS 团队合作监测模型质量 （漂移，降解）和功能健康 （新鲜度，数据质量）.定义警报和响应过程什么让你适合？• 3年以上在 ML 工程，数据工程或 DevOps 领域的经验，并参与生产中部署和维护 ML 系统；•使用实验跟踪和模型登记 （MLflow,W&B 等）来构建 ML 培训管道；·了解功能商店概念和火车服务一致性挑战；·强大的 Python 技能；对 ML 框架有足够的理解，包装，服务和调试模型。· 经验与 Docker 和 ML 管道编排 （Kubeflow,Argo Workflows,Metaflow 或类似）· 强的 SQL 技能和数据仓库架构理解 · B1或更高水平的英语语言以有效地沟通国际团队 我们的工作方式：·创新精神：致力于创造性和开创性的解决方案 ·诚实反：重视公开，透明的交流 ·支持团队：一个强大，协作社区 ·庆祝成就：共同承认我们的胜利 · 高科技
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We are looking for Senior MLOps Engineer to join our Risk team. Risk team is responsible for automated IT solutions in risk management processes. Within this process we solve the tasks for creating models and inference it on production in real-time with specified SLA, integrate tons of external services and make decisions only on data.
Our Data Science team develops scoring models including neural network-based approaches, and we need to industrialize the entire ML lifecycle. We are looking for an MLOps Engineer to build ML infrastructure from scratch — from automated training pipelines to a unified feature platform serving both DWH research and real-time production inference.
Challenges That Await You
- Conduct evaluation of Feature Store / Feature Registry solutions (Feast, Tecton, or custom), prepare a recommendation, design the architecture and feature lifecycle process (experiment → stable → production). Lead the implementation by engineering and platform teams
- Design, build, and own ML training and deployment pipelines: experiment tracking, model registry, CI/CD for models, packaging and handoff to production. Select the platform (MLflow or alternatives), establish versioning and validation standards, and evolve the infrastructure continuously
- Select tooling and set up monitoring of model quality (drift, degradation) and feature health (freshness, data quality) in collaboration with the DS team. Define the alerting and response process
What Makes You a Great Fit
- 3+ years of experience in ML Engineering, Data Engineering, or DevOps with hands-on involvement in deploying and maintaining ML systems in production
- Experience building ML training pipelines with experiment tracking and model registry (MLflow, W&B, or similar)
- Understanding of feature store concepts and train-serve consistency challenges
- Strong Python skills; understanding of ML frameworks enough to package, serve, and debug models
- Experience with Docker and ML pipeline orchestration (Kubeflow, Argo Workflows, Metaflow, or similar)
- Solid SQL skills and understanding of data warehouse architecture
- B1 or higher English level for effective communication with an international team
Our ways of working:
- Innovative Spirit: A commitment to creativity and groundbreaking solutions
- Honest Feedback: valuing open, transparent communication
- Supportive Team: a strong, collaborative community
- Celebrating Achievements: recognizing our wins together
- High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances
Our benefits:
- Relocation support to one of our hubs — Cyprus, Serbia, Georgia or Kazakhstan — with assistance for the employee and their family
- Flexible work from one of our offices or remote
- HealthcareCoverage
- Education Budget: Language lessons, professional training and certifications
- Wellness Budget: Mental health and fitness activity reimbursements
- Vacation policy: 20 days of annual leave and paid sick leave
Originally posted on Himalayas