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 escalate early instead of sitting on an uncertain production signal.

· 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.

If you meet the requirements and are interested in this exciting opportunity, apply at www.rehire.ar/jobs and send us your CV!