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Open nowFirst seen 29 hours ago

Senior Software Engineer (Backend, AI Platform)

inDriver150 open roles

Where
Islamabad, Islamabad Capital Territory
Work mode
Hybrid
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Your applicationOpen nowSenior Software Engineer (Backend, AI Platform)inDriver · Islamabad, Islamabad Capital Territory

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. inDriver postings stay open a median of 1 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: first seen 29 hours ago

inDriver median: 1 days open

The posting

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.

Key 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. 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.

Skills, Knowledge and Expertise

  • 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

Why join us

  • Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production.
  • A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft.
  • Help us challenge injustice by creating fair choices for millions of people across 48 countries.
  • Develop your professional skills with access to mentoring, career consulting, and learning programs.
  • Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
  • Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
  • Work alongside people who take initiative, speak openly, and challenge themselves to grow.
  • Improve your language skills through co-financed courses and internal speaking clubs.

Final benefits may vary depending on the location.

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