GenAI Platform Engineer

Position: GenAI Platform Engineer (Junior, ≤ 5 years’ post-graduation)

Key Responsibilities

• Build and own end-to-end pipelines for

  Large Language Model (LLM) fine-tuning and evaluation (LoRA, QLoRA, RLHF).

• Design and implement

  retrieval-augmented generation (RAG)

  systems, including vector-store schema, ingestion jobs, and latency-optimized query paths.
• Develop reusable agent workflows with frameworks such as L

  angChain, LangGraph,

  or custom orchestrators; write automated tests for prompts, tools, and guardrails.
• Create

  CI/CD release gates

  for model artifacts, prompts, and workflow graphs; integrate them with Git-based workflows and Kubernetes deployments.

• Instrument GenAI services

  with custom metrics

  (token usage, hallucination rate, safety scores)

  and surface them through Grafana dashboards and alert policies.
• Partner with Product, Research, and SRE teams to translate lab prototypes into cost-efficient, production-grade services that meet strict SLOs.

Required Qualifications

• Bachelor’s degree (or higher) in Computer Science, Engineering, or related field

  ; graduated within the past five years.
• Strong

  Python

  skills and a habit of writing clean, testable, production-ready code.
• Hands-on experience training or fine-tuning transformer-based models.

• Familiarity with at least one vector database or similarity search engine (FAISS, Milvus, Pinecone, Weaviate, etc.).

• Working knowledge of

  Docker, Kubernetes, and at least one major cloud provider.

• Excellent verbal and written communication skills in English; comfortable collaborating across multicultural, distributed teams.

Preferred Qualifications

• Experience building data or retrieval pipelines with K

  afka, OpenSearch/ElasticSearch,

  or equivalent.
• Familiarity with

  evaluation & guardrail frameworks (PromptLayer, LangSmith, Guardrails.ai).

• Understanding of

  GPU scheduling, mixed-precision training, and cost-optimization strategies

  .
• Contributions to

  open-source GenAI repositories

  or peer-reviewed publications.

• Korean

  language ability is a

  plus

  but

  not

  required.