Key skills
Generative AI modelsBackend software developmentPythonJavaAWS technologiesContainerization DockerContainerization KubernetesMicroservices architectureCI/CD toolsAgile development methodologiesDistributed data systemsAIMLGenerative AIKafkaAWSGCPKubernetesDockerJenkinsRESTfulGitPerformance OptimizationSaaSAgileCI/CD
About this role
Oscilar is building the most advanced AI Risk Decisioning™ Platform, and they are seeking a highly experienced Staff Software Engineer - Generative AI to join their backend team. In this role, you will design, build, and maintain AI-powered services that form the core infrastructure of their SaaS platform, directly impacting performance, reliability, and innovation.
Responsibilities:
- Design and implement scalable backend services that leverage generative AI models to deliver high-performance, low-latency solutions
- Collaborate with product, frontend, and QA teams to define technical requirements and ensure seamless integration of AI models with other platform components
- Optimize AI models and backend services for maximum performance, scalability, and maintainability in a distributed environment
- Identify and resolve bottlenecks related to AI processing and system performance, ensuring efficient resource utilization and system stability
- Implement best practices for deploying, monitoring, and maintaining AI models in production, including CI/CD pipelines and model versioning
- Proactively monitor the health and performance of AI-driven backend services, applying strategies to mitigate potential issues and ensure high availability
Requirements:
- Bachelor's or master's degree in computer science, Software Engineering, or a related field
- 7+ years of backend software development experience, with 3+ years focusing on AI/ML, including hands-on experience with generative AI models (e.g., GPT, LLMs)
- Strong expertise in Python or Java and AWS technologies, with deep knowledge of building and operating low-latency, high-scale services in distributed environments
- Proven experience deploying and optimizing AI/ML models in production environments using containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP)
- Familiarity with microservices architecture, RESTful APIs, and security best practices in AI services
- Experience with distributed data systems such as Kafka, ClickHouse, or similar technologies to manage large-scale AI workloads
- Strong understanding of CI/CD tools (e.g., Jenkins, Git, Maven, Gradle) and agile development methodologies, with a focus on automating model deployment
- Excellent problem-solving abilities, with a focus on AI model performance optimization, scalability, and system architecture