Staff Data Engineer - Office Of The CTO

Staff Data Engineer - Office Of The CTO

Sonatus is a well-funded, fast-paced, and rapidly growing company whose software products and solutions help automakers build dynamic software-defined vehicles. With over four million vehicles already on the road with top global OEM brands, our vehicle and cloud software solutions are at the forefront of automotive digital transformation. The Sonatus team is a talented and diverse collection of technology and automotive specialists hailing from many of the most prominent companies in their respective industries.

Role Summary:

We are seeking a highly experienced and strategic Staff Data Engineer to architect, build, and scale our data and MLOps platforms. In this role, you will be responsible for the full data lifecycle, from designing and building complex data pipelines to implementing the MLOps framework that enables our machine learning models to move from experimentation to production with velocity and reliability. You'll leverage deep expertise in data engineering and MLOps to make critical decisions that span data processing, model training, and serving. This is a key leadership position for a hands-on engineer who will define our data governance, model versioning, and production observability best practices.

Roles And Responsibilities:

• Architect the Unified Platform: Design and build the foundational, end-to-end platform for our Generative AI systems, making critical decisions that span data processing, vectorization, training, and model serving.
• Engineer the Data Engine: Build and scale the data pipelines that are the lifeblood of our models. This includes processing massive datasets for fine-tuning and creating the real-time data flows for our Retrieval-Augmented Generation (RAG) systems.
• Own the Production Lifecycle: Implement the full MLOps framework. You will build the CI/CD/CT (Continuous Integration/Delivery/Training) automation that takes models from experiment to production with velocity and reliability.
• Build the "AI Factory" Floor: Deploy, scale, and optimize our model serving infrastructure. You will manage GPU resources, minimize inference latency, and build the robust monitoring to ensure our AI is always fast, accurate, and cost-effective.
• Establish the Unified Playbook: Create a single, cohesive set of best practices for the entire AI lifecycle. Your work will define how we handle data governance, model versioning, infrastructure as code, and production observability in one seamless system.

Requirements:

• A seasoned engineer with 8+ years of experience building and scaling data-intensive, production-grade systems.
• A "systems thinker" with a demonstrated ability to architect end-to-end solutions, possessing a strong foundation in either Data Engineering or MLOps/SRE, with significant, hands-on experience in the other.
• Deep proficiency in Python and Infrastructure as Code (e.g., Terraform, Pulumi).
• Hands-on experience with cloud platforms (GCP, AWS, Azure) and containerization/orchestration (Docker, Kubernetes).
• Experience building either large-scale data pipelines (e.g., Spark, Airflow) OR robust ML deployment systems (e.g., Kubeflow, MLflow, model servers).
• Direct experience building systems for LLMs, especially RAG pipelines.
• Experience with vector databases (e.g., Pinecone, Weaviate) and embedding management.
• Experience with both data processing frameworks (e.g., Spark, Dagster) and MLOps tools (e.g., MLflow, Kubeflow, Vertex AI).
• Note: We don't expect you to be a world-leading expert in every tool. We are looking for a leader who understands the principles behind them and has experience building a representative set of these systems