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Designs and builds secure, scalable distributed backend systems using Python and AWS. Responsibilities include event-driven architectures, asynchronous processing, cloud data pipelines, serverless applications, REST APIs, performance optimization, authentication, and cloud security. The role requires strong distributed systems, testing, debugging, and backend development expertise, with GenAI, LLM, RAG, and vector database experience preferred.

Designs and builds scalable, secure distributed backend systems using Python and AWS. Responsibilities include event-driven architectures, asynchronous processing, cloud data pipelines, serverless applications, REST APIs, performance optimization, and cloud security. The role also involves testing and debugging distributed systems, with optional work integrating LLMs, RAG architectures, vector databases, and embedding-based search.

Design and build scalable Go services and APIs, optimize performance and reliability, debug concurrency and distributed-system issues, improve observability, maintain deployment pipelines, conduct code reviews, and mentor engineers. The role owns features end to end, from architecture and development through deployment and monitoring, while contributing to technical direction and engineering standards.

Supports B2B technology sales through prospect research, outbound outreach, lead qualification, customer engagement, solution positioning, CRM maintenance, and pipeline management. The role provides hands-on exposure to U.S. SMB sales, AWS, cloud, software, data, AI, and SaaS offerings while developing toward an independent technology sales career.

Designs and builds large-scale batch and real-time data pipelines, including ingestion, transformation, storage, data models, APIs, and event-driven architectures. The role involves hands-on coding in Python and SQL, cloud-native AWS workflows, performance tuning, data quality, observability, architecture, and code reviews. The Principal Data Engineer partners with AI, backend, and DevOps teams while mentoring engineers as a technical individual contributor.

Build and operate production Generative AI systems, including RAG pipelines, AI agent workflows, LLM integrations, and Python backend services. Deploy cloud-native applications using AWS, Kubernetes, Terraform, Helm, and CI/CD. Monitor, debug, test, and improve production systems while mentoring through code reviews and owning end-to-end technical delivery.

Design and implement scalable, secure distributed backend systems on AWS using Python. Build event-driven pipelines, asynchronous concurrent processing, serverless applications, and RESTful APIs (Flask/FastAPI). Optimize performance, implement cloud security and authentication (OAuth/JWT), and test/debug distributed architectures.