Senior Backend Engineer, AI Platform & ML Ops

inDrive; Pakistan; On-site; PKR 3,348,000 - 7,254,000; Full time

Job summary

inDrive is hiring a Senior Backend Engineer to design, build and operate the ML platform services in Go and Python. You will own components from design to on-call, collaborating with data scientists and ML engineers while advancing the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker).

You will ship reliable, scalable backend services for high-load workloads, focusing on observability, deployment pipelines and production readiness.

Qualifications

• 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.

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.
• 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.

Skills

Go

Python

SQL

Redis

gRPC

Docker

Kubernetes

AWS/GCP

Observability

Terraform

Tools

Databricks

SageMaker

Kubeflow

MLflow

Terraform

Terragrunt

Kafka

RabbitMQ

Prometheus

Grafana

ELK

Job description

inDrive is hiring a Senior Backend Engineer to design, build and operate the ML platform services in Go and Python. You will own components from design to on-call, collaborating with data scientists and ML engineers while advancing the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker).

You will ship reliable, scalable backend services for high-load workloads, focusing on observability, deployment pipelines and production readiness.