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