Teams at Pythian

Own end-to-end data platform architecture and implementation, including ingestion, lakehouse and warehouse layers, transformations, dimensional and semantic models, governance, access controls, and analytics delivery. Build and deploy Spark and SQL pipelines, data models, and integrations across databases, SaaS applications, APIs, files, and legacy systems. Select and implement Databricks, Microsoft Fabric, or both based on client requirements, while applying CI/CD, testing, and version control practices.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

Design, deploy, and operate large-scale distributed systems; manage Kubernetes, Istio, and Linux environments; automate workflows with Go/Python/shell; build observability with Prometheus/Grafana/Loki; troubleshoot networking, storage, and performance; participate in on-call rotations and postmortems; collaborate with AI/ML teams to ensure infrastructure readiness for model training and data pipelines.