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inDrive operates ML models across 48 countries with a rapidly growing platform. The company ships to Databricks, SageMaker, and self-managed GPU inference, with LLM workloads moving into production.
You will join as a Senior Backend Engineer to build and operate the services and tooling that ML teams depend on. This is a full-stack ownership role: you will design, build, and run backend services and internal tooling for the ML platform in Go and Python, owning them from design through on-call support. You will drive technical research and design for new platform features, weighing trade-offs and documenting decisions.
Key responsibilities include:
- Design and build backend services and internal tooling for the ML platform in Go and Python, with full production ownership including delivery, monitoring, and on-call rotations.
- 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 it through reviewed pull requests.
- Participate in code and design reviews, mentor engineers, and hold AI-generated changes to the same standard as human code.
You will work cross-functionally with data scientists, ML engineers, software engineers, and data engineers. You will learn the MLOps stack on the job and grow your expertise in production ML systems.
REQUIREMENTS:
- 5+ years of commercial backend engineering, including high-load systems.
- Strong Go: production experience with advanced language features.
- Solid Python for data processing (batch, streaming, async) and service code.
- Strong SQL: schema design, indexes, query optimization, 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, 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 with Linux command line, 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 MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow, 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 without an established playbook.
- Infrastructure-as-Code (IaC) tools experience.
