The Full Stack Software Engineer Stf designs, develops, deploys, and supports secure, scalable, and reliable software solutions across the full application lifecycle. The role is responsible for frontend and backend development, API and database design, cloud infrastructure, CI/CD pipelines, security controls, and production application stability.
This position will build and operate AI/ML-enabled web applications, integrating services such as model-serving APIs, LLM providers, vector-search tools, and internal machine-learning platforms. Responsibilities include translating business requirements into user-focused technical solutions; developing responsive interfaces and backend services; and ensuring applications are maintainable, performant, and secure.
The engineer will provide technical leadership in Agile delivery, infrastructure automation, deployment processes, monitoring, incident response, and reliability improvements. The role partners with product, data science, security, infrastructure, operations, and business stakeholders while mentoring engineers and establishing sound engineering standards.
Desired Skills
Experience delivering and operating AI/ML-powered web applications, including integrations with model-serving APIs, LLM providers, embedding or vector-search systems, and internal machine-learning platforms.
Familiarity with MLOps and LLMOps practices, including model and prompt lifecycle management, evaluation workflows, AI observability, performance monitoring, and safe rollout of AI capabilities.
Advanced experience owning production CI/CD and release processes across frontend, backend, infrastructure, and containerized services, including automated testing, security scanning, artifact management, deployment controls, and rollback strategies.
Hands-on experience with Infrastructure as Code and cloud environment management using tools such as Terraform, CloudFormation, Pulumi, Helm, or similar technologies.
Experience improving the reliability, scalability, and availability of cloud-native applications through Kubernetes operations, autoscaling, networking, secrets management, load balancing, monitoring, alerting, incident response, and capacity planning.
Strong knowledge of application, cloud, and AI security practices, including secure coding, OWASP guidance, IAM, OAuth/OIDC, secrets management, vulnerability remediation, dependency security, and protections for AI-specific risks such as prompt injection and sensitive-data exposure.
Experience with asynchronous and event-driven architectures, including message queues, background jobs, streaming workflows, serverless services, or similar patterns.
Proven technical leadership, including establishing engineering standards, mentoring engineers, and proactively managing production risks related to quality, performance, usability, reliability, and security.
