• Architect, build, and scale the foundational infrastructure powering our decentralized ML training platform • Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-code (Pulumi/Terraform) • Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes • Architect fault-tolerant infrastructure for distributed ML including GPU clusters, health monitoring, and resilient retry strategies • Build systems that simulate and handle real-world network conditions

• Design and implement large-scale distributed training systems optimized for heterogeneous hardware operating under low-bandwidth, high-latency conditions. • Develop and optimize model-parallel training strategies (data, tensor, pipeline parallelism) with custom sharding techniques that minimize communication overhead. • Optimize GPU utilization, memory efficiency, and compute performance across distributed nodes. • Implement robust checkpointing, state synchronization, and recovery mechanisms for long-running, fault-prone training jobs. • Build monitoring and metrics systems to track training progress, model quality, and system bottlenecks. • Architect resilient training systems where nodes can fail, networks can partition, and participants can dynamically join or leave. • Design and optimize peer-to-peer topologies for decentralized coordination across non-co-located nodes. • Implement NAT traversal, peer discovery, dynamic routing, and connection lifecycle management. • Profile and optimize communication patterns to reduce latency and bandwidth overhead in multi-participant environments.

• Empacotar e versionar modelos de machine learning (MLflow, SageMaker Model Registry) • Definir e implementar serviços AWS adequados (SageMaker, Lambda, ECS/EKS, API Gateway, entre outros) • Construir e manter esteiras CI/CD, garantindo automação de testes, build e deploy • Automatizar deploys em múltiplos ambientes (dev/staging/prod) com segurança e rollback • Expor modelos para consumo por outros serviços (via endpoints ou Lambdas) • Configurar e acompanhar monitoramentos em produção (CloudWatch, logs, métricas) • Colaborar com times multidisciplinares para garantir soluções eficientes, seguras e escaláveis.

• Develop, program, and test machine learning systems • Design architectures for reproducible, scalable, and monitored Machine Learning solutions • Assist the data science team in designing and building model deployment pipelines • Build data engineering processes/pipelines within the data environment • Define architecture standards, procedures, and tooling • Define data layers and their intended usage • Implement, fine-tune, and monitor LLM models (e.g., Bedrock, OpenSearch, HuggingFace) • Build inference pipelines for RAG-based responses (Retrieval-Augmented Generation) • Ensure versioning and reuse of trained models and embeddings • Work on continuous improvement of response relevance and source ranking • Work on reducing token costs using optimization techniques • Support integrations with AI APIs, vector stores, and document repositories • Communicate information clearly with technical and business team members • Help identify potential errors and report inconsistencies