Senior Software Engineer (Backend, AI Platform)

inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production.

We are hiring a Senior Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers.

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.

5+ 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