Sunscrapers

• Build AI-powered infrastructure that gives VC and PE clients a genuine edge — proprietary systems that extract signals, automate decisions, and compound in value over time. • Agentic AI Systems: Multi-step LLM workflows, RAG pipelines, and agent orchestration systems — owned from architecture to production. • Full-Stack AI Applications: Client-facing web applications with AI embedded throughout — Python/FastAPI backends, React frontends, integrated with LLM providers (OpenAI, Anthropic, Gemini). • Data Platform Engineering: Scalable pipelines and cloud infrastructure (AWS/GCP) that underpin AI features — vector databases, data ingestion layers, API integrations. • Technical Discovery & Client Engagement: Translating business needs into AI-first technical proposals. • AI Quality & Internal Standards: Guardrails, automated testing, and observability for AI systems.

• Crafting beautiful UI and styling the web application using Tailwind CSS • Develop and maintain dynamic web applications using HTMX with the Django framework • Implementing and integrating more compound web components • Ensure consistent application of UX best practices to deliver intuitive and user-centered interfaces • Close collaboration with the team (Designer and Backend Engineer) to make sure implementation meets requirements • Co-develop and co-maintain web applications using Python and Django • Implement CI/CD pipelines using CircleCI • Monitor and debug issues using Sentry • Attention to detail and sense of aesthetics, from clean code to beautiful UI

• Design and scale the platform's core infrastructure, ensuring it can handle massive data volumes with precision and reliability. • Define and evolve infrastructure and support platform architecture for key products and services. • Drive a multi-year roadmap for reliability, security and support platform capabilities. • Establish reference architectures and golden paths that product and data teams can adopt. • Lead adoption of SRE practices: SLOs/SLIs, error budgets, incident management, and post-incident reviews. • Design and maintain observability (metrics, logs, traces, alerts, dashboards) across services and infrastructure. • Automate runbooks and self-healing mechanisms to reduce toil and improve MTTR. • Design and evolve a self-service platform (environments, CI/CD, infra templates) that helps teams ship safely and quickly. • Own and improve Infrastructure as Code (IaC) and GitOps workflows. • Build reusable platform components and tools that standardize how services are built, deployed, and operated. • Partner with Security and Compliance to embed zero-trust, least privilege, and encryption into infrastructure patterns. • Support healthcare-grade compliance (e.g., SOC 2, HIPAA, HITRUST) through infrastructure design and automation. • Collaborate with data teams on secure, reliable data platforms and pipelines. • Lead FinOps practices: visibility into spend, cost optimization, and guardrails that balance cost and reliability. • Design for resilience and recovery: backup/restore, DR, and regional failover strategies. • Continuously tune performance and capacity across compute, storage, and networking.

• Test case design, • UI/E2E automation, • Regression testing, • Ensuring sprint-level quality, • Test applications manually, when needed, • Taking care of the quality of the application and the testing process - promoting good practices in the team, • Use mostly Selenium/Cypress & Azure DevOps Test Plans

• Developing PoCs using latest technologies, experimenting with third party integrations • Owning and evolving Next.js/React frontend with authenticated flows and secure session handling • Delivering production grade applications once PoCs are validated • Creating solutions that enable data scientists and business analysts to be self-sufficient as much as possible • Designing and implementing secure, scalable access patterns (OAuth2/OIDC, authorization boundaries) • Finding new ways how to leverage Gen AI applications and underlying vector and graph data storages • Contributing across the stack including FastAPI backend services and agent-driven workflows • Contributing data technology stacks including data warehouses and ETL pipelines • Building data flows for fetching, aggregation and data modeling using batch and streaming pipelines • Documenting design decisions before implementation

• Write clean, scalable, and secure code using T-SQL and .NET/C# programming languages for applications hosted on-prem and in Azure • Develop front-end and back-end components using Angular, .Net Core, C#, T-SQL, Javascript, Message Queues, and related technologies. • Design, implement, and maintain efficient database schemas, stored procedures, and queries using SQL Server. Optimize database queries and web code for performance and stability. • Revise, update, refactor, and debug code to troubleshoot errors and improve the code • Follow Agile methodology of software development, participate in the team’s Agile ceremonies (Standup, Retro, Planning, Refinement, etc.). • Participate in requirements analysis and refinement – update requirements as needed • Produce and execute unit tests. • Participate in code reviews and provide as well as incorporate feedback • Collaborate with internal teams to produce and document scalable and consistent software design and architecture • Contribute to improving development processes, engineering tools, code quality, automation, and the software development lifecycle (SDLC). • Serve as an expert on applications and provide technical support. • Work on Helpdesk Tickets as needed to provide Level 2/3 support

• Your primary goal is to build AI-powered infrastructure that gives VC and PE clients a genuine edge — proprietary systems that extract signals, automate decisions, and compound in value over time. • Agentic AI Systems: Multi-step LLM workflows, RAG pipelines, and agent orchestration systems — owned from architecture to production. From quick experiments to full production deployments — you know when to move fast and validate, and when to engineer for scale. Real clients depend on these systems to make investment decisions. • Full-Stack AI Applications: Client-facing web applications with AI embedded throughout — Python/FastAPI backends, React frontends, integrated with LLM providers (OpenAI, Anthropic, Gemini). Claude Code or Cursor is your primary environment. AI-assisted coding is your default mode, not a shortcut you reach for occasionally. • Data Platform Engineering: Scalable pipelines and cloud infrastructure (AWS/GCP) that underpin AI features — vector databases, data ingestion layers, API integrations. The foundation that makes everything else work. • Technical Discovery & Client Engagement: You’ll translate business needs into AI-first technical proposals — in the room with CFOs, GPs, and operating partners. You know what’s possible and you can explain it to someone who doesn’t write Python. • AI Quality & Internal Standards: Guardrails, automated testing, and observability for AI systems. You’ll help define what ‘good’ looks like across every engagement — contributing to internal engineering standards that compound over time.

• Modeling datasets and schemes for consistency and easy access, • Design and implement data transformations and data marts, • Integrating third-party systems and external data sources into data warehouse, • Building data flows for fetching, aggregation and data modeling using batch pipelines.

• Owning and evolving Next.js/React frontend with authenticated flows and secure session handling • Designing and implementing secure, scalable access patterns (OAuth2/OIDC, authorization boundaries) • Building and maintaining FastAPI backend services for agent-driven workflows and data integrations • Developing PoCs using latest technologies, experimenting with third party integrations • Delivering production grade applications once PoCs are validated • Creating solutions that enable data scientists and business analysts to be self-sufficient as much as possible • Finding new ways how to leverage Gen AI applications and underlying vector and graph data storages • Working with data warehouses, databases, and building data flows for fetching and aggregation • Contributing to infrastructure-as-code and AWS-based systems • Documenting design decisions before implementation