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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 experience in high-load systems and the ML lifecycle. Proficient in building observability tools and CI/CD pipelines while collaborating effectively with cross-functional teams.
Highest-signal resume keywords
Go ProgrammingPython ProgrammingSQL Schema DesignAWS/GCP Core ServicesCI/CD Pipelines
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 SystemsData ProcessingObservability DesignInfrastructure as CodeModel Performance MetricsTechnical Trade-OffsData PreparationMonitoring and Performance MetricsDeployment Tooling
Soft Skills
CollaborationMentoringCommunication
Tools & Technologies
DockerKubernetesRedisPrometheusGrafanaELKTerraformTerragruntGRPCKafka
Industry Keywords
ML LifecycleMLOpsCI/CDData CaptureModel Serving
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 using Go and Python.
- Own services in production, including delivery, monitoring and on-call.
- Drive technical research and design for new platform features, documenting trade-offs.
- Build and extend deployment tooling and SDKs for model serving, autoscaling and data capture.
- Integrate ML services with backend systems using synchronous and asynchronous communication.
- Support the 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 through reviewed pull requests.
- Participate in code and design reviews, mentor engineers, and review AI-generated changes to the same standard as human 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, including advanced features used in production.
- Solid Python for data processing and service code.
- Strong SQL: schema design, indexes, query optimisation, replication and partitioning.
- Experience with Redis or similar key-value stores.
- Experience with HTTP, gRPC, Kafka or RabbitMQ, focusing 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, including data preparation, model performance metrics, non-functional requirements, and concept drift.
- Familiarity with scikit-learn, PyTorch/TensorFlow.
- Experience designing and running observability with metrics, logging, dashboards, tracing, alerting and runbooks.
- Experience with Prometheus, Grafana, ELK or similar.
- Git workflows, code review, Linux command line, and networking basics including TCP/IP, DNS, VPC and load balancers.
- Ability to explain technical trade-offs clearly to data scientists, product owners and on-call engineers.
- Nice to have: MLOps tooling such as Databricks, SageMaker, Kubeflow, MLflow or Airflow; GPU or LLM inference; AI coding agents; emerging markets; Infrastructure-as-Code 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; platform-side teaching provided.
- Develop professional skills with access to mentoring, career consulting, and learning programs.
- Global Talent Exchange Program for international experience.
- 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.