About inDrive
inDrive is a global tech company whose services include ride-hailing, freight, delivery, intercity travel, and financial services. Its core business supports local communities through a fair pricing model, alongside impact programs.
Responsibilities
•Design, build, and operate backend services and internal tooling for the ML platform in Go and Python, owning production delivery, monitoring, and on-call support
•Research and design new platform features, evaluate trade-offs, and document decisions
•Build and extend deployment tooling and SDKs for ML teams to ship models, including serving, autoscaling, and data capture
•Integrate ML services with the wider backend through reliable synchronous HTTP and gRPC communication and asynchronous Kafka and RabbitMQ communication
•Support the ML delivery cycle with data scientists and ML engineers, including CI/CD for models and pipelines, monitoring, and model performance metrics
•Build end-to-end observability across metrics, logs, dashboards, tracing, alerting, and runbooks
•Write infrastructure as code with Terraform and Terragrunt and ship it through reviewed pull requests
•Participate in code and design reviews, help raise engineering standards, mentor engineers, and apply the same review standards to AI-generated changes as to human-written changes
Requirements
•5+ years of commercial backend engineering experience, including high-load systems
•Strong production experience with advanced Go features
•Solid Python skills for batch, streaming, and asynchronous data processing and service code
•Strong SQL skills, including schema design, indexes, query optimization, replication, and partitioning
•Experience with Redis or similar key-value stores
•Experience with synchronous and asynchronous service communication, including HTTP, gRPC, Kafka, or RabbitMQ, with a focus on reliability
•Experience with Docker and CI/CD pipelines, and working knowledge of Kubernetes
•Experience with core AWS or GCP services, including compute, storage, networking, and IAM
•Understanding of the ML lifecycle, including data preparation, model performance metrics, non-functional requirements, and concept drift in production
•Experience designing and operating observability, including metrics, logging, dashboards, and tracing with Prometheus, Grafana, ELK, or similar tools
•Familiarity with Git workflows, code review, the Linux command line, and networking basics including TCP/IP, DNS, VPC, and load balancers
•Ability to clearly explain trade-offs to data scientists, product owners, and on-call engineers
Nice to have
•Hands-on experience with MLOps tooling such as Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow, or Airflow
•GPU or LLM inference experience with vLLM, Triton, or Langfuse
•Experience using AI coding agents with full review of generated changes
•Experience in emerging markets or building without an established playbook
•Experience with Infrastructure-as-Code tools
•Familiarity with scikit-learn, PyTorch, or TensorFlow
Benefits
•Access to mentoring, career consulting, and learning programs
•International experience through the Global Talent Exchange Program
•Company-wide challenges, awards, sports activities, employee-led social impact, and volunteering projects
•Co-financed language courses and internal speaking clubs
•Final benefits may vary depending on the location
