Junior SRE Engineer Guadalajara Mexico

OpenKrill; United States; Sur place; USD 70 000 - 100 000; Plein temps

Résumé du poste

Rehire is seeking a Junior SRE Engineer to join its Reliability Engineering team. You will support a regulated consumer-lending platform on AWS across hundreds of microservices, contributing to AI-driven incident triage and governance.

Under senior guidance, you’ll help operate AI SRE sub-agents, implement monitors, and write automation in Python/Bash. This is a growth role with learning in AI-driven reliability practices and CI/CD integration.

Qualifications

• Bachelor's degree in CS, Engineering or related field is required.
• 1–3 years in SRE/DevOps, cloud infra, or production support.
• Advanced English (oral and written) required.
• Hands-on AWS experience across compute, networking, storage, and managed DB services.

Responsabilités

• Assist incident response as a shadow/secondary responder using AI-driven tools.
• Build incident timelines from metrics, logs, traces, and deploy history; document postmortems.
• Operate and monitor AI SRE sub-agents and flag anomalies for review.
• Develop Datadog monitors, dashboards, and SLOs as code using Terraform.

Connaissances

English proficiency

Python/Bash scripting

Git and PR workflows

AI/ML for SRE curiosity

Automation mindset

Incident response understanding

Formation

Bachelor's degree in CS/Engineering or related

Outils

AWS

Datadog

Terraform

Docker/Kubernetes

CI/CD tools (GitHub Actions, Jenkins)

Description du poste

Role Overview

At Rehire, we are partnering with a US-based data engineering and cloud technologies company to find a Junior SRE Engineer to join its Reliability Engineering team. You will be embedded with the SRE function of a financial services client, supporting a regulated consumer-lending platform on AWS with hundreds of microservices and event-driven pipelines, where reliability directly impacts customer trust and compliance.

This is a unique opportunity to grow in an environment where AI is already part of day-to-day operations, including an AI SRE co-pilot, purpose-built AI agents for incident triage and monitoring, and a formal AI governance program. Working under the guidance of senior engineers and architects, you will help operate, improve, and learn from this AI-driven reliability program.

Key Responsibilities:

• Support incident response as a shadow or secondary responder, using AI-driven detection, correlation, and root-cause analysis tools.
• Help build incident timelines from metrics, logs, traces, and deploy history, and document postmortems and corrective actions through to closure.
• Help operate and monitor AI SRE sub-agents (incident summarization, monitor-gap detection, usage attribution), flagging anomalies for senior review.
• Build and maintain Datadog monitors, dashboards, and SLO definitions as code using Terraform, and support SLI/SLO and error-budget reviews for critical customer journeys.
• Help reduce alert noise with AI/ML-assisted detection (anomaly, outlier, and forecast monitors, dynamic thresholds) and run recurring monitor-hygiene reviews.
• Support the day-to-day reliability of AWS workloads (ECS/Fargate, EKS, Lambda, RDS/Aurora, ALB, SQS/SNS, Step Functions).
• Identify capacity, saturation, and cloud cost anomalies, and help attribute spend and telemetry volume to owning teams and services.
• Write automation in Python and Bash against platform APIs (Datadog, AWS, GitHub, PagerDuty, Jira) and contribute Terraform modules through pull requests.
• Help integrate reliability controls and AI-assisted checks into CI/CD pipelines, and create runbooks progressively automated toward self-healing.
• Participate in architecture, reliability, and AI-risk reviews, learning how compliance frameworks (PCI-DSS, SOC 2, SOX, GLBA) apply in a regulated environment.

Requirements:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 1–3 years of experience in SRE, DevOps, cloud infrastructure, platform, or production-support engineering.
• Advanced English (oral and written). REQUIRED
• Hands-on exposure to AWS (or an equivalent hyperscaler) across compute, networking, storage, and managed database services.
• Exposure to at least one observability platform (Datadog preferred; Grafana/Prometheus, New Relic, CloudWatch, or ELK/OpenSearch also relevant).
• Foundational understanding of SLI, SLO, and error-budget concepts.
• Foundational knowledge of containers and orchestration (Docker, ECS, or Kubernetes) and serverless execution models.
• Beginner-to-intermediate experience with Infrastructure as Code (Terraform preferred; Ansible or CloudFormation acceptable).
• Scripting experience in Python, Bash, or similar, including consuming REST APIs and parsing JSON.
• Basic Linux troubleshooting and networking fundamentals (DNS, TLS, load balancing, timeouts, and retries).
• Comfort with Git, pull-request workflows, and CI/CD tools (GitHub Actions, Jenkins, GitLab CI, ArgoCD, or similar).
• Familiarity with incident management and on‑call concepts (severity models, escalation policies, PagerDuty or Opsgenie).
• Experience in product engineering services, enterprise software, or fintech is a plus.
• Awareness of compliance frameworks (PCI‑DSS, SOC 2, SOX, GLBA) is a plus.

Key Competencies:

• Automation mindset: you would rather automate a task the second time you do it than the tenth.
• Good judgment to escal
• Clear written communication: you can explain an incident, a metric, or a trade‑off to someone who was not in the room.
• Curiosity about LLM‑based assistants and agents applied to operations, and about how to verify that their output is correct.

Preferred Certifications (not required):

• AWS Certified Cloud Practitioner or an Associate‑level AWS certification.
• HashiCorp Certified: Terraform Associate.
• Datadog Fundamentals or an equivalent observability certification.
• Certified Kubernetes Administrator (CKA) or KCNA.

About the Position:

• Work Schedule: US shift presential at Guadalajara, Mexico.
• Work Modality: Full-time contractor basis.

Competitive Salary Paid in USD.

Work Environment: Dynamic and collaborative.

Professional Growth: Hands‑on learning in AI‑driven SRE practices and opportunities for career advancement.