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Top Skills:
Agentic Ai FrameworksDockerGoLlmMongoDBPostgresPrompt EngineeringPythonRetrieval-Augmented Generation (Rag)
Cloud • Fintech • Software • Business Intelligence • Consulting • Financial Services
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Top Skills:
AnalyticsCreatioCRMData MigrationErpHubspotIntegrationsMarketing Automation
Cloud • Fintech • Software • Business Intelligence • Consulting • Financial Services
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Top Skills:
CreatioHubspot
Build and operate high-load backend services and internal tooling for a global ML platform using Go and Python. Develop model deployment tooling, SDKs, service integrations, CI/CD, observability, infrastructure as code, and production on-call capabilities. Collaborate with data scientists and ML engineers across the model lifecycle, including serving, monitoring, performance metrics, and reliability. Lead technical design, conduct reviews, mentor engineers, and support Kubernetes, cloud, MLOps, GPU, and LLM workloads.
The position is based in Georgia (the country, not the U.S. state of Georgia)
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.
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.
About
inDrive is a global tech company on a mission to challenge injustice through fair choices. We started in 2012 in the coldest city on Earth, when a group of friends created a way for people to agree on fair ride prices. That idea grew into one of the world’s top ride-hailing apps, now with 400M installs across 47 countries.Today, we offer more than rides: from freight and delivery to intercity travel and financial services, all designed to put people first. We are committed to having a positive impact on people’s lives both through our core business, which supports local communities via a fair pricing model; and through the work of our impact programs. Ready to ignite your inner drive?
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