FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
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 experience in deploying and monitoring ML services. Proficient in CI/CD practices, observability, and infrastructure as code, ensuring high reliability and performance in high-load systems.
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 CodeData PreparationModel Performance MetricsNetworking BasicsRedisDocker
Soft Skills
MentoringTechnical Communication
Tools & Technologies
TerraformKubernetesPrometheusGrafanaELKKafkaRabbitMQGitDatabricksSageMaker
Industry Keywords
MLOpsMachine Learning LifecycleData CaptureAutoscalingPerformance Metrics
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; evaluate and document trade-offs.
- Build and extend deployment tooling and SDKs for ML 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 with 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, raise engineering standards, and mentor 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.
- 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 such as 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.