Platform Engineer (Backend, AI Platform, MLOps)

Описание

inDrive is hiring a Platform Engineer in Cairo to build backend services and internal tooling for its ML platform. The role focuses on deployment, serving, observability, CI/CD, and infrastructure-as-code for models and data pipelines using Go, Python, Kubernetes, Terraform, and cloud services.

Responsibilities

• Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call
• Drive technical research and design for new platform features. Weigh trade-offs and write them down
• Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture
• Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load
• Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics
• Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks
• Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests
• Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones

Requirements

• 3+ years of commercial backend engineering, including high-load systems
• Strong Go: you know and have used its advanced features in production
• Solid Python for data processing (batch, streaming and async) and for service code
• Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning
• Experience with Redis or similar key-value stores
• Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability
• Docker, CI/CD pipelines, and working Kubernetes knowledge
• Experience with AWS/GCP core services (compute, storage, networking, IAM)
• Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production
• Familiarity with scikit-learn, PyTorch/TensorFlow
• Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar)
• Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers)
• Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer

Nice to have

• Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow
• GPU or LLM inference experience (vLLM, Triton, Langfuse)
• Experience using AI coding agents with full review of generated changes
• Experience in emerging markets, or building where there is no playbook yet
• Experience with Infrastructure-as-Code (IaC) tools

We offer

• Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production
• A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft
• Help us challenge injustice by creating fair choices for millions of people across 48 countries
• Develop your professional skills with access to mentoring, career consulting, and learning programs
• Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program
• Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects
• Work alongside people who take initiative, speak openly, and challenge themselves to grow
• Improve your language skills through co-financed courses and internal speaking clubs
• Final benefits may vary depending on the location