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inDrive operates ML models across 48 countries with a rapidly growing platform. The company deploys to Databricks, SageMaker, and self-managed GPU inference, with LLM workloads moving into production. We are hiring a Senior Backend Engineer to build and operate the services and tooling that ML teams depend on.
You will design, build, and run backend services and internal tooling for the ML platform using Go and Python, owning services end-to-end from design through production monitoring and on-call support. Key responsibilities include:
• Design and build backend services and internal tooling for the ML platform in Go and Python, with full ownership including delivery, monitoring, and on-call responsibilities.
• Drive technical research and design for new platform features, documenting trade-offs clearly.
• Build and extend deployment tooling and SDKs that ML teams use to ship models, including serving, autoscaling, and data capture.
• Integrate ML services with the wider backend using sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication patterns, optimized 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 using Terraform and Terragrunt, shipping changes through reviewed pull requests.
• Participate in code and design reviews, mentor engineers, and maintain consistent review standards for both human and AI-generated changes.
You will work cross-functionally with data scientists, ML engineers, software engineers, and data engineers, learning the MLOps stack on the job.
REQUIREMENTS:
• 5+ years of commercial backend engineering experience, including high-load systems.
• Strong Go proficiency with demonstrated use of advanced features in production.
• Solid Python for data processing (batch, streaming, async) and service code.
• Strong SQL: schema design, indexes, query optimization, and scaling approaches (replication, partitioning). Experience with Redis or similar key-value stores.
• Experience with sync and async service communication (HTTP, gRPC, Kafka, RabbitMQ) with 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 systems: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar).
• Git workflows and code review proficiency, comfortable with Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers).
• Ability to explain trade-offs clearly to data scientists, product owners, and on-call engineers.
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 established playbook.
• Infrastructure-as-Code (IaC) tools experience.
