Site Reliability Engineer

Intermedia Cloud Communications

. Run and improve production environments supporting AI workloads, data pipelines, analytics applications, and customer-facing services .

Posted 10/5/2026full-timePortugalMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in managing and optimizing production environments for AI workloads and data pipelines, with a strong focus on reliability, performance, and automation. Proficient in cloud infrastructure, CI/CD practices, and data quality management to ensure seamless operation of analytics applications and services.

Highest-signal resume keywords

Cloud Infrastructure ManagementCI/CD AutomationData Processing TechnologiesProduction Operations ExperienceTroubleshooting Across Distributed Systems

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Production OperationsSystems EngineeringSREDevOpsData EngineeringSoftware DeploymentDistributed SystemsData Quality ChecksPerformance TestingMonitoring and Observability

Soft Skills

Analytical Problem-SolvingCross-Functional CommunicationProactive Reliability Improvement

Tools & Technologies

KubernetesKafkaSparkAirflowDbtCloud ServicesLinuxAPIsData WarehousesMonitoring Tools

Industry Keywords

AI WorkloadsData PipelinesAnalytics ApplicationsService-Level IndicatorsOperational StandardsIncident ResponseDataOpsMLOpsBatch and Streaming WorkloadsModel-Serving Infrastructure

Tech Stack

Tools & technologies

AirflowCloudDistributed SystemsKafkaKubernetesLinuxSpark

About the role

Key responsibilities & impact

Run and improve production environments supporting AI workloads, data pipelines, analytics applications, and customer-facing services
Build software and automation for cloud infrastructure, data platforms, model-serving infrastructure, and application services
Define and measure SLIs, SLOs, and error budgets for AI and analytics services
Build end-to-end observability across metrics, logs, traces, data quality, AI performance, and customer impact
Monitor and optimize reliability, performance, capacity, and cost of batch and streaming workloads, analytics queries, and inference services
Partner with data engineering and machine-learning teams to productionize ingestion, transformation, feature, training, deployment, and reporting workflows
Automate CI/CD and production-readiness checks for pipelines, model and prompt releases, schema changes, analytics applications, and dashboards
Detect and resolve data-quality incidents, schema drift, and broken lineage or dependencies
Design and test graceful degradation, dependency isolation, retry/fallback patterns, and recovery procedures
Improve reliability of AI-powered Voice and Unified Communications capabilities
Monitor model and AI-service behavior, including latency, throughput, error rates, drift, and output quality
Plan capacity and run performance, load, and resilience tests
Lead incident response and post-incident improvements
Support secure and reliable access to cloud storage, processing, and query services
Reduce operational toil through platform tooling, runbooks, self-service automation, and operational standards

Requirements

What you’ll need

Bachelor's degree in computer science, data engineering, software engineering, or another technical or scientific discipline, or equivalent practical experience
4-7 years of experience in production operations, systems engineering, SRE or DevOps, software deployment, and maintenance of distributed production systems
Experience with cloud infrastructure, containers, Kubernetes, distributed systems, and scalable compute and storage services
Experience operating data processing, orchestration, storage, or analytics technologies such as Kafka, Spark, Airflow, dbt, data warehouses, or comparable cloud services
Ability to use metrics, logs, traces, data-quality checks, freshness indicators, lineage, and service-level indicators to diagnose complex production issues
Experience with CI/CD, DataOps or MLOps practices, automated testing, controlled rollout, and rollback of data and AI service changes
Strong troubleshooting skills across Linux, applications, networks, APIs, data pipelines, and distributed service dependencies, with attention to security and access controls
Strong analytical problem-solving and cross-functional communication skills, with a proactive approach to reliability, performance, and continuous improvement

Benefits

Comp & perks

Equal opportunity employer and commitment to diversity and inclusion
Reasonable accommodations for identified disabilities or other limitations as required by applicable laws
Remote work flexibility
Promotion-from-within opportunities