Lead development of scalable backend services and platform infrastructure for an AI agent platform. Responsibilities include architecture and code reviews, infrastructure automation, CI/CD, observability, reliability, distributed systems, ML/NLP workload support, and mentoring engineers. The role requires expertise in Java, Python, cloud environments, Docker, Kubernetes, infrastructure-as-code, monitoring tools, and data infrastructure.
Role Overview:
Vectara is seeking a Senior/Staff Platform Software Engineer with strong experience in backend development and modern DevOps practices. In this role, you’ll work on building and scaling the core infrastructure that powers our AI agent platform. You’ll help design robust services and scalable systems, with a focus on automation, observability, reliability, and performance.
Key Responsibilities:
- Proficiency in AI-assisted coding; able to do multiple tasks at once and manage AI agents to execute quickly and thoughtfully.
- Lead code, system design, and architecture reviews while teaching and mentoring team members.
- Design, implement, and maintain scalable and secure backend services and platform components.
- Drive infrastructure automation, CI/CD, and monitoring/alerting pipelines.
- Collaborate with product engineering and research teams to deploy, scale, and optimize ML/NLP workloads.
- Lead initiatives to improve system reliability, scalability, and developer experience.
- Contribute to architecture and infrastructure decisions as we scale our platform.
- Champion best practices in code quality, testing, and DevOps culture across the team.
Qualifications:
- 5+ years of experience as a software engineer with a focus on backend systems and platform engineering.
- Proficiency in Java and Python.
- Deep experience with all computing environments (GCP, AWS, Onprem, or Azure).
- Strong understanding of containerization and orchestration (Docker, Kubernetes).
- Experience with infrastructure-as-code (Terraform, Pulumi, etc.)
- Experience with observability tools (Prometheus, Grafana, ELK/EFK, etc.).
- Experience working on ML platforms or supporting ML workloads in production.
- Experience designing and operating distributed systems.
- Familiarity with data infrastructure (e.g., Kafka, Spark, Airflow).
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