Sobek

Full-stack Engineer

• Own features across the stack. Take ideas from conception to production: UX flows → React/TypeScript UI → well‑scoped Python/TypeScript APIs/services → production rollout and measurement.
• Build intuitive, data‑dense interfaces. Tables, complex forms, document views, comparisons for modern Gen AI workflows, while keeping these accessible and responsive.
• Design and operate services. Create clear REST/gRPC/streaming endpoints, model data in SQL/NoSQl + vector stores, use queues/caches when it helps, and add tracing/metrics.
• AI orchestration. Extend orchestration that reliably manages multi‑LLM workflows with the right controls for cost, latency, and security/auditability.
• Ship quickly, iterate with users. Prototype, launch behind flags, collect feedback/telemetry, and refine.
• Collaborate deeply. Partner with engineers across the stack to translate real needs into reliable software.
• Be a force multiplier. Drive reviews and ADRs across frontend and backend and set patterns others can scale.

Full-Stack Engineer, AI

Frontend Engineer

• Build core frontend architecture. Lead the design and implementation of data-intensive web apps with a strong focus on accessibility and reliability.
• Define and prototype user experience. Take ambiguous, high-stakes workflows from concept to launch. Create wireframes, user flows, and lightweight interactive prototypes to iterate with the team and users before hardening into production.
• Establish design systems. Create reusable component libraries with clear documentation and robust UI tests.
• Ship real-time, resilient UX. Build interfaces that handle large content, streaming results, and intermittent networks—with graceful error recovery.
• Collaborate on API & data contracts. Work with backend teams to define REST/gRPC/streaming semantics, caching, error handling, and retry/backoff behavior that make the UI performant.
• Operate at the boundary when needed. Where it accelerates delivery, implement lightweight server routes (TypeScript or Python) to compose services for the UI: auth context, pre-signed uploads/downloads, and server-side streaming.
• Instrument and iterate. Wire UX telemetry and user feedback into evaluation loops; partner with AI engineers to improve time-to-insight and reduce cost.
• Build for enterprise constraints. Partner on authN/authZ and multi-tenant needs; uphold frontend security hygiene in regulated environments.
• Serve as a force multiplier. Drive code reviews and ADRs, and set patterns others can scale.

Frontend Engineer – AI Product

• Architect core platform functionality including distributed systems and event-ingestion pipelines
• Engineer production-grade AI systems managing complex workflows and non-determinism
• Instill security and reliability by designing enterprise-grade security into services
• Lead through engineering excellence with thoughtful code reviews and documentation.

AI Software Engineer

AI Engineer

• Design reliable AI workflows: Build multi-step, tool-using agents with typed tool contracts, deterministic routing, self-checks, and guardrails
• Context engineering: Implement selection, compression, isolation, structured memory, query planning, and retrieval strategies, with metrics and debuggability
• Orchestration & correctness: Own state machines/graphs for agent steps; backoffs/retries; idempotency
• Interoperability: Add adapters to invoke external agents and let our capabilities be invoked on the other end, (e.g., headless/MCP); enforce typed contracts and least-privilege context across boundaries
• Compute in the loop: Orchestrate computational/AI models (simulation, optimization, classic ML, foundation models) alongside LLM steps; schedule long-running jobs with provenance, caching, and resumability
• Ship production systems end-to-end: Stand up services, write clean REST/gRPC APIs, wire queues/workers, add feature flags, and harden for scale
• Cost, latency, quality: Optimize context budgets, caching, model/tool selection, track and improve SLOs
• Make AI observable & safe: Instrument traces/metrics/logs across LLM calls, tools, retrieval, and compute; build offline/CI evaluation harnesses (faithfulness, retrieval quality, safety, task success) that gate deploys.