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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in backend engineering with a strong focus on Go and Python, alongside proficiency in SQL and cloud services. Capable of driving the ML delivery cycle, integrating services, and ensuring observability through effective tooling and monitoring.
Highest-signal resume keywords
Go ProgrammingPython Data ProcessingSQL Schema DesignCI/CD PipelinesAWS/GCP Core Services
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Backend EngineeringHigh-Load SystemsService CommunicationObservability DesignInfrastructure as Code
Soft Skills
Mentoring EngineersCollaboration
Tools & Technologies
DockerKubernetesRedisTerraformPrometheus
Industry Keywords
ML LifecycleData PreparationModel Performance MetricsMLOps ToolingAI Coding Agents
Tech Stack
Tools & technologiesAirflowAWSDNSDockerGoogle Cloud PlatformGrafanaGRPCKafkaKubernetesLinuxPrometheusPythonPyTorchRabbitMQRedisScikit-LearnSQLTCP/IPTensorflowTerraformGo
About the role
Key responsibilities & impact- Design, build and run backend services and internal tooling for the ML platform in Go and Python, owning delivery, monitoring and on-call.
- Drive technical research and design for new platform features, weighing and documenting trade-offs.
- Build and extend deployment tooling and SDKs for model serving, autoscaling and data capture.
- Integrate ML services with the wider backend using synchronous HTTP/gRPC and asynchronous Kafka/RabbitMQ communication.
- Support the full ML delivery cycle with data scientists and ML engineers, including model and pipeline CI/CD, monitoring and performance metrics.
- Build end-to-end observability covering metrics, logs, dashboards, tracing, alerting and runbooks.
- Write infrastructure as code with Terraform and Terragrunt through reviewed pull requests.
- Participate in code and design reviews, mentor engineers, and maintain review standards for AI-generated changes.
- Collaborate with data scientists, ML engineers, software engineers and data engineers.
Requirements
What you’ll need- 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 including Databricks, SageMaker, Kubeflow, MLflow or Airflow.
- Nice to have: GPU or LLM inference experience (vLLM, Triton, Langfuse).
- Nice to have: experience using AI coding agents with full review of generated changes.
- Nice to have: experience in emerging markets, or building where there is no playbook yet.
- Nice to have: experience with Infrastructure-as-Code (IaC) tools.
Benefits
Comp & perks- 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.