ooca - Telemedica Co., Ltd

Data Engineer

• Design, build, and optimize LLM pipelines including retrieval, context assembly, grounding, prompting, and tool-calling
• Develop RAG systems using vector databases, metadata design, chunking strategies, and hybrid retrieval approaches
• Build agentic workflows and function-calling systems capable of reasoning, planning, and executing tasks
• Evaluate model performance using structured test suites, synthetic QA generation, and benchmark datasets
• Work closely with product and domain experts to translate ambiguous requirements into clear, structured LLM tasks
• Diagnose and mitigate failure modes such as hallucinations, retrieval misses, and context collisions
• Apply lightweight fine-tuning or LoRA for task-specific improvements when needed
• Develop and maintain backend components for LLM pipelines, including FastAPI, event systems, and queues
• Prepare unstructured documents such as PDFs, HTML, logs, and knowledge bases for ingestion and indexing in vector/RAG systems
• Support deployment across cloud or on-prem infrastructure using vLLM, TGI, Ollama, GPUs, and token-budgeting
• Ensure AI features are grounded, safe, testable, reproducible, versioned, and backed by real evaluation

AI Engineer, Data Specialist

Product Manager

• Lead structured product discovery, define problems, map journeys, prioritize opportunities, and validate solutions before building
• Run lightweight 3–5 day or one-week discovery cycles from problem framing through prototyping, user validation, and decision-making
• Translate research into product insights, constraints, and tradeoffs across clinical, compliance, operational, and commercial considerations
• Own metric-driven iteration across acquisition, activation, conversion, retention, revenue, and referral
• Define measurement plans, events, funnels, cohorts, dashboards, and product instrumentation
• Run experiments including hypotheses, success metrics, rollout plans, and post-analysis
• Define product strategy and roadmap aligned with company OKRs, capacity, and financial outcomes
• Evaluate initiatives for revenue uplift, retention improvement, cost reduction, operational efficiency, and risk reduction
• Maintain prioritization systems such as RICE, ICE, or Opportunity Scoring and communicate tradeoffs with C-level stakeholders
• Collaborate with engineering, design, data, clinical/operations, sales, marketing, and customer success
• Align internal and external stakeholders through discovery sessions, feedback loops, and documentation of scope, timelines, and success metrics
• Oversee releases and coordinate launch readiness, enablement, communications, and support preparation
• Create PRDs, user stories, acceptance criteria, edge cases, and non-functional requirements with developers and designers
• Maintain backlog quality, sprint ceremonies, QA readiness, bug triage, release monitoring, and post-release learnings
• Ensure event tracking, dashboards, and experiment setup are built into product delivery
• Use AI to accelerate research synthesis, PRD drafting, user-story refinement, analytics assistance, experiment design, QA test cases, release notes, and stakeholder communications
• Partner with engineering on AI-enabled estimation, test planning, triage, documentation, and release workflows
• Enforce privacy, security, accuracy, and risk guardrails, especially for mental health use cases
• Build a customer-driven culture of continuous discovery, fast learning loops, crisp writing, and decision clarity
• Build, mentor, or manage PM and UX/UI teams as needed and raise product-practice quality