Solutions Architect Lead

As the Solutions Architect Lead, you will own the solution and software architecture across research projects within our Rio de Janeiro R&D hub, working as one team with TGS Data Science, TGS software engineering teams and TGS architects in Houston. You will set the architectural direction for products that bridge machine learning and deterministic, physics-based geoscience — ML-driven and conventional processing, imaging and interpretation, the MDIO open-source data format and the TGS Data Verse platform — on GPU and CPU HPC clusters and in the cloud. Your primary objective is to turn research into robust, high-performance products that TGS and its clients rely on, in support of the TGS Strategy.

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Responsibilities

• Team Leadership: Lead, mentor and grow a team of scientific software developers, and coordinate architecture with data scientists, geophysicists and TGS software teams across projects, setting clear technical goals, coding standards and development paths.

• Technical Direction: Define the product and solution architecture across research projects, so that ML models and deterministic, physics-based algorithms work together as one product — shared data flows, interfaces, validation and HPC/cloud workflows — in line with the TGS Data Science and software roadmaps: one shared codebase and toolset.

• Architecture Governance: Maintain reference architectures and design standards, lead design reviews across research projects together with TGS software architects, and own build-vs-reuse and technical-debt decisions so research projects build on common, reusable components.

• AI Development: Partner with data scientists to architect the training, scaling, validation and deployment of deep learning models for seismic processing, imaging and interpretation, from seismic foundation models (ViT) to 3D CNNs such as SaltNet.

• Performance & Scalability: Drive the profiling, parallelization and optimization of training and inference on GPU and CPU clusters (CUDA, MPI, multi-node) and on AWS, keeping compute efficient and costs under control.

• Data Platforms: Champion MDIO and cloud-native data practices — chunked, compressed data accessible in place — and integration with TGS Data Verse (Data Lake, OSDU) and Prediktor operational data.

• Production Readiness: Own the path from prototype to product — packaging, testing, CI/CD, model registry and deployment into TGS production software (e.g., Imaging AnyWare) and HPC environments.

• Engineering Excellence: Establish best practices across research and production code: clean architecture, code review, automated testing, documentation and reproducible experiments.