CÔNG TY CỔ PHẦN SMARTOSC

The kinds of problems you'll solve

❖    Taking LLM-based systems (RAG pipelines, multi-agent workflows, conversational assistants) from "works in a demo" to reliable under real-world, messy, adversarial, or incomplete input.

❖    Building the evaluation and quality layers most AI teams lack: regression testing for prompts/agents, hallucination and safety checks, retrieval quality measurement, latency/cost/quality tradeoffs.

❖    Adding the operational maturity that's usually missing from AI prototypes: observability, alerting, retries/fallbacks, security review, and incident-readiness.

❖    Improving cloud infrastructure and deployment pipelines so services scale, restart cleanly, and cost less to run.

❖    Deciding, system by system, what "production-ready" actually requires — and pushing back when a shortcut isn't safe to ship.

❖    Mentoring other engineers and raising the bar on testing, code quality, and documentation across the team.

Tech stack you'll work in

❖    AI/LLM engineering: LLM application development, RAG, agentic/tool-using systems, prompt engineering, evaluation and guardrails.

❖    Backend: Python (modern tooling — package managers like uv, linters like Ruff), with some Node/TypeScript.

❖    Frontend: React-based frameworks (Next.js-style), with internationalization considerations.

❖    Cloud & infrastructure: AWS (containers, serverless functions, CDN/static hosting), infrastructure-as-code, Docker.

❖    Quality & delivery: End-to-end testing (Playwright or similar), backend test suites, CI/CD pipelines.