14 часов назад
Applied AI Research Engineer (AI)
Мэтч & Сопровод
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Описание вакансии
Текст:
TL;DR
Applied AI Research Engineer (LLM systems): Building and shipping reliable AI-backed features for a customer-feedback intelligence platform with an accent on evaluation, production engineering, and end-to-end ownership. Focus on designing retrieval pipelines and agents, debugging failures across data, infrastructure, models, and prompts, and hardening systems through monitoring and regression testing.
Location: Bengaluru, India
Company
builds an AI-native platform that centralizes customer feedback from surveys, reviews, support tickets, and communities to generate actionable product insights.
What you will do
- Design, build, and ship AI-backed features that remain reliable in production.
- Define quality standards through evaluation rubrics, test plans, measurable criteria, and rollout requirements.
- Write and extend production Python code, implement monitoring, and add regression tests.
- Own features end to end, from problem framing and modeling through system design, rollout, and iteration.
- Debug failures across data, infrastructure, models, and prompt logic, then harden systems based on findings.
- Design retrieval pipelines, agents, and hybrid AI systems while collaborating with product, infrastructure, and engineering.
Requirements
- Experience building and shipping AI systems, including supporting them after launch.
- Strong research instincts and the ability to define success and design evaluations that reflect real-world usage.
- Strong Python engineering skills, including writing clean, testable code and debugging production systems.
- Experience working with modern language models through prompting, fine-tuning, tooling, and evaluation.
- Systems-oriented thinking covering interfaces, data contracts, failure modes, and rollout plans.
- Ownership, clear communication, initiative, and a practical focus on shipping reliable systems.
Culture & Benefits
- Research is applied directly to production systems rather than limited to papers.
- Quality practices include evaluation plans, incident retrospectives, monitoring, and per-tenant guardrails.
- Small, focused team working on high-impact and ambiguous engineering challenges.
- Engineers own meaningful problems and follow them through to reliable implementation.
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