Lead Site Reliability Engineer

Design, build, and optimize cloud and data infrastructure to ensure the high availability, reliability, and scalability of big-data and ML/AI systems to meet customer needs, while implementing SRE principles such as monitoring, alerting, error budgets, and fault analysis.

Collaborate closely with cross-functional teams, including customers, development, product management, and security teams, to create secure, scalable solutions that support ML/AI workloads and enhance operational efficiency through automation.

Troubleshoot complex technical problems in production environments, perform root cause analyses, and contribute to continuous improvement efforts through postmortem reviews and proactive performance optimization.

Lead the architectural vision and shape the team s technical strategy and roadmap, balancing immediate needs with long-term goals, driving innovation, and influencing the technical direction.

Serve as a mentor and technical leader, guiding teams and fostering a culture of engineering and operational excellence by sharing your deep knowledge and experience.

Engage with customers and stakeholders to understand use cases and feedback, translating them into actionable insights and effectively influencing stakeholders at all levels.

Utilize your strong programming skills to integrate software and systems engineering, building core data platform capabilities and automation to meet enterprise customer needs and roadmap objectives.

Develop strategic roadmaps, processes, plans, and infrastructure to efficiently deploy new software components at an enterprise scale while enforcing engineering best practices.

Ability to design and implement scalable and well tested solutions, with focus on operational efficiency.

Strong hands-on cloud experience, preferably AWS.

Infrastructure as a Code expertise, especially Terraform and Kubernetes/EKS.

Hands-on expertise scaling and orchestrating production AI/ML infrastructure using the Hadoop ecosystem (Spark, Hive, HDFS, Gobblin), Airflow , and AWS big data services like EMR and SageMaker .

Ability to write high quality code in Python, Go, or equivalent programming languages.

Solid understanding of Unix/Linux systems, the kernel, system libraries, file systems, and client-server protocols.

Have experience with architecting software and infrastructure at scale with a sense of ownership and accountability.

Experience with observability tools including Prometheus (Alertmanager), Grafana, Thanos, CloudWatch, OpenTelemetry, and the ELK stack.

Certifications: CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer), AWS Certified DevOps Engineer, or equivalent certifications in cloud and security domains.