29 days ago
Egypt (city not specified)Senior
Responsibilities
- Design, build, and operate production backend services and internal tooling for the ML platform using Go and Python.
- Research and design new platform features, document technical trade-offs, and participate in code and design reviews.
- Build deployment tooling and SDKs for model serving, autoscaling, and data capture.
- Integrate ML services with backend systems using synchronous and asynchronous communication while maintaining reliability under high load.
- Support the ML delivery lifecycle through model and pipeline CI/CD, monitoring, and model performance metrics.
- Build end-to-end observability including metrics, logs, dashboards, tracing, alerting, and runbooks.
- Create infrastructure as code with Terraform and Terragrunt and ship it through reviewed pull requests.
- Mentor engineers and maintain consistent review standards for human- and AI-generated code changes.
- Own services from design through delivery, monitoring, on-call support, and production operations.
Requirements
- 5+ years of commercial backend engineering experience, including high-load systems.
- Strong production experience with Go and its advanced features.
- Solid Python experience for data processing and service development, including batch, streaming, and asynchronous workloads.
- Strong SQL skills covering schema design, indexes, query optimization, replication, and partitioning, plus experience with Redis or similar key-value stores.
- Experience with HTTP, gRPC, Kafka, or RabbitMQ for synchronous and asynchronous service communication.
- Working knowledge of Docker and Kubernetes, CI/CD pipelines, and AWS or GCP core services including compute, storage, networking, and IAM.
- Understanding of ML lifecycle concepts including data preparation, model performance metrics, non-functional requirements, and concept drift.
- Familiarity with scikit-learn, PyTorch, or TensorFlow.
- Experience designing and operating observability systems using metrics, logging, dashboards, tracing, Prometheus, Grafana, ELK, or similar tools.
- Experience with Git workflows, code review, Linux command line usage, and networking fundamentals including TCP/IP, DNS, VPCs, and load balancers.
- Ability to explain technical trade-offs clearly to data scientists, product owners, and on-call engineers.
- Preferred experience with Databricks, Unity Catalog, Asset Bundles, SageMaker, Kubeflow, MLflow, Airflow, GPU or LLM inference, vLLM, Triton, Langfuse, AI coding agents, and infrastructure-as-code tools.
- Experience in emerging markets or in building systems without an established playbook is preferred.
Benefits
- Mentoring, career consulting, and learning programs.
- Global Talent Exchange Program and opportunities for international collaboration and experience.
- Company-wide challenges, awards, sports activities, employee-led social impact, and volunteering programs.
- Co-financed language courses and internal speaking clubs.
- Final benefits may vary depending on location.
Tech Stack
Apache AirflowApache KafkaAWSDatabricksDockerGitGoGoogle Cloud PlatformGrafanagRPCKubernetesLinuxMLflowPrometheusPythonPyTorchRabbitMQRedisscikit-learnSQLTensorFlowTerraform
Categories
About inDrive
inDrive builds a global mobility and urban services marketplace best known for ride-hailing, with additional offerings in intercity rides, delivery, and financial services. The privately held company is headquartered in Mountain View, California, and operates across 48 countries, monetizing through fees on transactions in its app. Its platform emphasizes driver–rider price negotiation and has amassed hundreds of millions of downloads.