• Work closely with research engineers to develop productionized ML workflows • Develop and iterate on platform infrastructure for training, validating, and deploying neural networks • Deploy cloud infrastructure and build distributed data-processing pipelines • Develop on an in-house multi-tenant data lake ecosystem supporting the entire lifecycle of machine learning operations • Build internal APIs to serve data, trigger workflows, enforce business logic, etc. • Build CI/CD process and other internal tools to enhance developer productivity

• Configure and maintain AWS services including VPCs, EC2 instances, queues, DocumentDB, load balancers, security groups, Kubernetes clusters, and container repositories, along with network security controls (firewalls, WAF, and network ACLs). • Build and manage Jenkins jobs and CI/CD pipelines that produce deployment artifacts, including ZIP packages and Docker images; maintain Kubernetes deployment configurations (kubectl, YAML). • Write Linux utility scripts to support cluster management, automate deployments and operational tasks, and troubleshoot infrastructure issues across distributed environments. • Develop and maintain applications in C#, T-SQL, Vue, HTML, and CSS; troubleshoot issues across the full stack including UI, API, database, messaging, and deployment layers. • Build and maintain core application services on ServiceStack and the LLBLGen ORM data-access layer; customize and support platform-integrated applications such as Umbraco and Identity Server. • Maintain and troubleshoot integrations between internal systems and external platforms; manage Git-based source control workflows (GitHub/GitLab) including branching, code reviews, and coordinated deployments. • Work with SQL Server and T-SQL for querying, troubleshooting, and supporting application data. • Support MongoDB/DocumentDB document storage and reason about relational and document database usage across application services. • Design and support event-based, message-driven architecture using MassTransit over message brokers such as Amazon SQS, RabbitMQ, and Apache MQ across distributed application services. • Apply AI-assisted development tools (e.g., GitHub Copilot, Devin) to coding, debugging, documentation, and PR workflows to measurably improve team productivity. • Review, validate, and govern AI-generated code and infrastructure-as-code; evaluate emerging AI productivity tools and recommend adoption across engineering teams. • Adapt deployment processes and engineering practices as platform requirements and tooling evolve. • Mentor and guide engineers across the stack, raising the bar on code quality, architecture, and operational practices. • Lead technical decisions and provide hands-on guidance on complex, cross-cutting problems from development through production troubleshooting. • Partner with engineering leadership to set technical direction, plan coordinated releases, and onboard and grow team members.

• Serve as the most senior individual contributor on TTG’s engineering team. • Take end-to-end ownership of cloud infrastructure, CI/CD pipelines, and full-stack application support. • Set technical direction across the stack — from AWS infrastructure and Kubernetes operations to C#/.NET development, database and messaging systems, and AI-augmented engineering workflows. • Dedicate a meaningful share of your time to mentoring engineers, guiding technical decisions, and shaping how the team builds. • Configure and maintain AWS services such as VPCs, EC2, DocumentDB, and Kubernetes clusters. • Build and manage Jenkins jobs and CI/CD pipelines. • Develop and maintain applications in C#, T-SQL, Vue, HTML, and CSS; troubleshoot issues across the full stack. • Work with SQL Server and T-SQL for querying and supporting application data. • Apply AI-assisted development tools to coding, debugging, documentation, and PR workflows.

• Desenvolver soluções backend robustas e escaláveis, utilizando Java e Spring Boot. • Participar ativamente do ciclo de vida completo do desenvolvimento de software, desde a concepção até a entrega em produção. • Implementar e evoluir pipelines de CI/CD, garantindo automação e qualidade nas entregas. • Realizar testes unitários, integrados e E2E, promovendo práticas de qualidade e confiabilidade. • Integrar e gerenciar bancos de dados relacionais, além de atuar com Kafka e seus conectores. • Trabalhar com JPA/Hibernate e Kubernetes para orquestração de aplicações. • Garantir a observabilidade nas aplicações, utilizando métricas, logs e monitoramento para evoluir continuamente a operação. • Aplicar e disseminar boas práticas de resiliência de software, assegurando sistemas tolerantes a falhas e alta disponibilidade. • Colaborar com outros times, promovendo alinhamento e comunicação clara. • Propor soluções inovadoras para desafios técnicos e de negócio. • Documentar arquitetura e decisões técnicas, facilitando o entendimento e evolução das soluções. • Contribuir para o desenvolvimento técnico de outros engenheiros do time. • Buscar e aplicar feedbacks para melhoria contínua, mantendo postura colaborativa e proativa.