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# Site Reliability Engineer

- **Company:** BOSTON PUBLIC HEALTH COMMISSION
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $145,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Cloud Computing, Computer Programming, Continuous Integration, DevOps, Distributed Systems, Fault Tolerance, Github, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Key Management, Network Segmentation, Octopus Deploy, Open Source Technology, Reliability Engineering, Prometheus, Runbook, Software Engineering, Datadog, Pulumi, Cloud Platform System, Real Time Systems, Grafana, Mttr, Multi-Cloud, Reliability of Systems, HybridCloud, Cloudformation, Kubernetes, Low Latency, Deployment Automation, Machine Learning Operations, Terraform, Dynatrace, Devsecops, Docker, Jenkins, Vulnerability Analysis, Microservices
- **Published:** June 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5c83684cbe530e29

## About the Role

Do you have experience in Tooling?, 3+ years in Site Reliability Engineering, DevOps, or infrastructure roles
Experience owning production systems at scale
Strong background in incident response and postmortem processes
Experience in fast-paced startup or high-growth environments

Technical Skills
Strong programming ability in Python, Go, or similar
Deep experience with AWS, GCP, or Azure infrastructure
Strong Kubernetes and Docker expertise in production environments
Experience with Infrastructure as Code (Terraform, Pulumi, CloudFormation)
Hands-on experience with CI/CD systems (GitHub Actions, ArgoCD, Jenkins, etc.)
Strong knowledge of observability tools (Prometheus, Grafana, Datadog, ELK)
Understanding of distributed systems and system design principles

Soft Skills
Strong reliability-first mindset with focus on failure scenarios
Calm and structured thinking during production incidents
Clear communication across technical and non-technical teams
Strong documentation and runbook writing ability
High ownership and autonomy in system management

Preferred Qualifications
Experience with AI/ML infrastructure or GPU-based workloads
Background in real-time systems or low-latency architectures
Experience with chaos engineering practices
Knowledge of AI observability (model latency, drift, throughput metrics)
Multi-cloud or hybrid cloud experience
Contributions to open-source infrastructure or SRE tooling
Early-stage startup experience (Seed to Series A), Suggested improvements for reliability or performance
Optional: GitHub, SRE projects, or technical writing samples

## Description

We are hiring a proactive, systems-minded Site Reliability Engineer to ensure that LockedIn AI's production systems remain highly reliable, scalable, and performant.

This role sits at the intersection of infrastructure, software engineering, and operations. You will be responsible for maintaining the reliability of real-time AI systems that serve over 1 million users globally.

You will own production stability across cloud infrastructure, Kubernetes environments, AI inference systems, APIs, and real-time services.

Key Responsibilities
System Reliability & Performance
Own reliability, availability, and performance of production systems
Define and manage SLIs, SLOs, and error budgets aligned with product goals
Design fault-tolerant and self-healing system architectures
Continuously optimize system latency, throughput, and resource usage
Identify bottlenecks and improve system performance at scale

Infrastructure as Code & Cloud Systems
Design and manage cloud infrastructure across AWS, GCP, or Azure
Implement Infrastructure as Code using Terraform, Pulumi, or CloudFormation
Manage Kubernetes clusters supporting microservices and AI workloads
Ensure infrastructure is versioned, reproducible, and scalable
Optimize cloud costs while maintaining performance and reliability

Observability & Monitoring
Build observability systems using metrics, logs, and traces (Prometheus, Grafana, Datadog, ELK, etc.)
Design alerting systems that reduce noise and improve incident response
Implement distributed tracing for microservices and AI pipelines
Monitor AI-specific metrics including latency, GPU usage, and token throughput
Provide real-time visibility into system health and performance

Incident Response & Reliability Engineering
Lead incident response for production outages and system failures
Participate in on-call rotations and coordinate cross-team resolution efforts
Conduct blameless postmortems with actionable improvements
Build runbooks, playbooks, and escalation procedures
Track MTTR and reliability trends to drive continuous improvement

CI/CD & Release Engineering
Design and maintain CI/CD pipelines for safe and automated deployments
Implement canary releases, blue-green deployments, and rollback systems
Ensure AI model deployments follow the same reliability standards as application code
Add testing and validation gates to prevent production issues
Improve deployment velocity while maintaining system stability

Security & Compliance
Implement secure infrastructure practices including IAM, encryption, and secrets management
Maintain network segmentation and audit logging
Ensure privacy-first infrastructure design across all systems
Manage vulnerability scanning, patching, and system hardening
Collaborate with security teams to enforce DevSecOps practices


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