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Divar

AI & Big Data Platform Engineer

Divar

. Design, build and operate the big data platform, including lakehouse, query engines and orchestration, focusing on reliability, performance and cost .

Posted 11/4/2026full-timeTehran • IranMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in building and operating big data platforms, including LLM-serving infrastructure and MLOps capabilities. Proficient in Kubernetes, Docker, and big data technologies, with a strong focus on data governance and security best practices.

Highest-signal resume keywords
KubernetesDockerMLOpsPythonBig Data Technologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Platform EngineeringData InfrastructureLLM Fine-TuningData GovernanceProblem-Solving
Soft Skills
Clear CommunicationEffective TeamworkOwnership
Tools & Technologies
GitOpsArgoCDHelmTerraformAirflowMLflowJupyterHubCoderPrometheusGrafana
Industry Keywords
Data QualityAccess ControlPrivacyPII AnonymizationGPU Infrastructure

Tech Stack

Tools & technologies
AirflowAnsibleDockerGrafanaJavaKafkaKubernetesPrometheusPythonSparkTerraformVaultGo

About the role

Key responsibilities & impact
  • Design, build and operate the big data platform, including lakehouse, query engines and orchestration, focusing on reliability, performance and cost
  • Build and run LLM-serving infrastructure on GPU clusters using vLLM on Kubernetes, including capacity planning, benchmarking and model rollouts
  • Operate and evolve the AI gateway with provider routing and failover, virtual keys and quotas, observability and tracing with Langfuse, and team-level usage and cost tracking
  • Build fine-tuning and MLOps infrastructure, including training jobs on on-premise and rented GPUs, experiment tracking, an MLflow model registry and a path from experiment to production
  • Provide self-service development environments using JupyterHub and Coder for data analysis and model development
  • Provide infrastructure for data governance, data quality, access control and privacy, including PII anonymization, across the data and AI lifecycle
  • Own monitoring, on-call and incident response for operated services
  • Build self-service platforms enabling business data analysts, software engineers and ML engineers to ship independently

Requirements

What you’ll need
  • Bachelor's degree in Computer Science, Computer Engineering or a related field, or equivalent experience
  • Proven experience as a Platform, Infrastructure/SRE, Data Infrastructure, MLOps or similar engineer
  • Strong Kubernetes and Docker skills, including running stateful services in production
  • Experience with GitOps and infrastructure as code: ArgoCD, Helm, Ansible, Terraform/Pulumi
  • Hands-on experience with big data technologies such as Kafka, Spark, Trino, Iceberg, Ceph, S3 and orchestration tools such as Airflow
  • Proficiency in Python
  • Solid understanding of LLMs: inference, context and tokens, prompting, RAG, and basics of fine-tuning
  • Experience setting up, maintaining and customizing open-source tools such as Airflow, JupyterHub, Coder, DataHub, MLflow and LiteLLM
  • Good understanding of data governance and security best practices
  • Strong problem-solving skills and proactive approach to technical challenges
  • Clear communication and effective teamwork
  • Ownership of what you build
  • Nice to have: experience with Go or Java
  • Nice to have: familiarity with the ML lifecycle, model training workflows and MLOps tools such as MLflow
  • Nice to have: experience with LLM serving tools and GPU infrastructure, including vLLM, SGLang, TensorRT-LLM, NVIDIA drivers, GPU scheduling on Kubernetes and multi-GPU inference
  • Nice to have: observability tools such as Prometheus/VictoriaMetrics and Grafana, and secret management with Vault

Benefits

Comp & perks
  • Your work will power data and AI features used by millions of people
  • Work on hard, modern problems, from large-scale data to running LLMs on the company's own GPU fleet
  • Team culture valuing craftsmanship, speed and innovation
  • Flexible working hours
  • Health insurance
  • Culture that values learning and autonomy
  • Work with people who care about doing great work and enjoy it