• Own the design and delivery of AI-powered automation tools that streamline creative production for enterprise teams. • Translate customer problems into scalable technical solutions in close collaboration with consultants and stakeholders. • Integrate AI models, including LLMs, text-to-image, and text-to-voice into production-grade creative tools. • Architect and build microservice-based systems that balance speed, scalability, and maintainability. • Prototype rapidly, then harden successful proofs-of-concept into reliable production applications. • Partner with designers and product managers to deliver seamless integrations with tools like Figma and Adobe. • Identify repeatable patterns across engagements and help codify them into reusable solutions and platforms.

• Design, build, and operate backend services in Python that power AI-driven products and shared capabilities. • Build and maintain resilient service integrations across internal and external systems, handling failure modes, rate limits, and interface changes. • Own ambiguous, end-to-end problems: from early design and architecture through implementation, rollout, and iteration in production. • Develop and evolve LLM-backed features and agentic workflows used in production, with a focus on reliability, observability, and real-world behavior. • Contribute to core AI platforms and enablement systems—services that your team uses directly and that other engineers can build on. • Collaborate with product managers, researchers, and other engineers across teams to turn loosely defined AI use cases into concrete, production-ready systems. • Help shape evaluation and quality strategies for AI-powered features, including building or extending evaluation harnesses, benchmarks, or feedback loops. • Act as a technical leader for the work you own—making sound design decisions, coordinating with stakeholders, and contributing to design and code reviews.

• Architect, implement, and deliver next-generation features for Cloudera’s Data Engineering Experience • Be a core contributor to Apache Spark • Develop high-performance features using Scala, Java, and Python on modern data platforms • Gain and apply expert-level knowledge in core distributed data processing concepts • Develop a deep technical understanding of components across the Cloudera Data Engineering Experience • Work hands-on with massive distributed systems, scaling from hundreds to thousands of nodes in live production clusters • Conduct thorough root cause analysis, debug complex system-level deployment issues, and resolve failures • Improve internal infrastructure and tooling to streamline development, testing, and deployment processes • Collaborate with a high-impact, distributed team and stakeholders to drive product vision and delivery

• Build and maintain features across the TP2.0 platform — from React microfrontend screens to TypeScript serverless Lambdas and Fastify services. • Contribute to the shared TypeScript libraries consumed by both frontend and backend, including its C# code-generation pipeline. • Write and maintain unit, integration, and E2E tests using Jest, Playwright, and K6. • Develop and deploy infrastructure changes using Terraform targeting AWS (Lambda, ECS, API Gateway, RDS/PostgreSQL, DynamoDB, S3, CloudFront, Secrets Manager). • Participate in GitLab CI/CD pipeline development and maintenance, including security scanning and Terraform deployment pipelines. • Collaborate with architects and product teams to evolve async job patterns (imports, report generation), calculation service APIs, and data layer abstractions. • Use AI coding assistants effectively — writing precise prompts, reviewing AI-generated code critically, and applying outputs productively within a large existing codebase. • Maintain and improve the legacy ASP.NET Framework 4.7.2 application as needed during migration.