Role summary

We're seeking a motivated, and passionate Site Reliability Engineering (SRE) leader with strong expertise in programming, distributed systems, AWS infrastructure and services, and Kubernetes. In this role, you'll help evolve our SRE team's Kubernetes and Service Mesh architecture, while also supporting the integration of AI workloads both within Kubernetes and via managed services.

The SRE function plays a critical role in maintaining system visibility, ensuring platform scalability, and enhancing operational efficiency. As part of this, you'll help drive AIOps initiatives, leveraging AI tools and automation to proactively detect, diagnose, and remediate issues, enhancing the reliability and performance of TraceLink’s global platform. As an SRE leader, you’ll have the opportunity to apply your technical strengths, shape platform reliability strategies, and collaborate closely with engineering teams across the organization. You’ll work as part of a globally distributed, inclusive team focused on AWS-based cloud infrastructure.

Key Responsibilities

SRE Leadership:

• Guide a team of SREs through weekly sprint planning and execution, helping them stay focused on delivery and long-term goals.

• Build a team environment centered around trust, ownership, and continuous learning.

• Partner with engineers across Platform and Application product teams to ensure what’s pushed to production is stable, secure, and reliable.

• Stay directly involved in technical work, contributing to the codebase and leading by example in solving complex infrastructure challenges.

Core SRE:

• Collaborate with development teams, product owners, and stakeholders to define, enforce, and track SLOs and manage error budgets.

• Improve system reliability by designing for failure, testing edge cases, and monitoring key metrics.

• Boost performance by identifying bottlenecks, optimizing resource usage, and reducing latency across services.

• Build scalable systems that handle growth in traffic or data without compromising performance.

AI Ops:

• Design and implement scalable deployment strategies optimized for large language models like LLaMA, Claude, Cohere, and others.

• Set up continuous monitoring for model performance, ensuring robust alerting systems are in place to catch anomalies or degradation.

• Stay current with advancements in MLOps and Generative AI, proactively introducing innovative practices to strengthen AI infrastructure and delivery.

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Monitoring and Alerting:

• Proactively identify and resolve issues by leveraging monitoring systems to catch early signals before they impact operations.

• Design and maintain alerting mechanisms that are clear, actionable, and tuned to avoid unnecessary noise or alert fatigue.

• Continuously improve system observability to enhance visibility, reduce false positives, and support faster incident response.

• Apply best practices for alert thresholds and monitoring configurations to ensure reliability and maintain system health.

• Incorporate agentic capabilities to monitor and proactively resolve system issues before they impact customers

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Cost Management

• Monitor infrastructure usage to identify waste and reduce unnecessary spending.

• Optimize resource allocation by using right-sized instances, auto-scaling, and spot instances where appropriate.

• Implement cost-aware design practices during architecture and deployment planning.

• Track and analyze monthly cloud costs to ensure alignment with budget and forecast.

• Collaborate with teams to increase cost visibility and promote ownership of cloud spend.

Required Qualifications:

• Bachelor’s degree in computer science, Engineering, or related field.

• 7+ years in SRE, DevOps, or cloud infrastructure; 3+ years managing SRE/DevOps teams responsible for large-scale, highly available, microservice-based systems.

• Deep knowledge of core operating system concepts, networking fundamentals, and systems management.

• Strong understanding of cloud-native deployment and management practices, especially in AWS.

• Strong expertise with AWS services from both a technical and cost optimization perspective.

• Hands-on experience with Terraform/OpenTofu, Helm, Docker, Kubernetes, Prometheus, and Istio.

• Proficiency in diagnosing and resolving container performance issues using modern tools and techniques.

• Hands-on experience with MLOps tools (Kubeflow, MLflow, SageMaker, Vertex AI, or equivalent).

• Familiarity with ML concepts: model lifecycle, feature stores, drift detection, and monitoring.

• Experience deploying, monitoring, and scaling AI/ML models, including LLM-based and agentic AI applications, in production.

• Skilled in modern DevOps/SRE practices, including CI/CD build and release pipelines.

• Experience with mature development processes, including source control, security best practices, and automated deployment.

• Familiarity with MLOps practices, including the deployment, monitoring, and scaling of AI/ML models in production, particularly LLM-based applications.

• Excellent written and verbal communication skills.

• Strong analytical and problem-solving abilities, with a bias for proactive issue identification and resolution.

Preferred Qualifications:

• Experience managing large-scale ML inference workloads, including LLM and agentic AI, in production.

• Knowledge of distributed training frameworks (TensorFlow, PyTorch).

• Hands-on development experience in Python and/or Golang.

• Experience managing SRE teams for 24/7, follow-the-sun operations.

• Familiarity with service mesh patterns beyond Istio (e.g., Linkerd, Consul).

• Experience managing GPU-enabled infrastructure and optimizing model-serving performance.

• Background in designing or implementing disaster recovery and business continuity plans.

• Prior experience in a regulated or compliance-heavy industry (e.g., healthcare, finance, life sciences).