Improveit Solutions

We are looking for a Senior AI/ML Engineer experienced in multimodal AI pipelines, LLM evaluation frameworks, agent orchestration, and scalable Python backend systems on AWS.

A Korean company is building and hardening an AI verification and compliance layer for AIGC/UGC financial content.

The system includes:

• four evaluation/verification models
• an automated compliance rule engine
• a Trust & Safety moderation pipeline

The core architecture processes video-based content through a multi-stage pipeline:

STT → OCR → image/video analysis → financial rules engine → LLM-based decision layer

Final output decision types:

• Auto-Block
• Auto-Pass
• Human Review

This role requires strong production engineering skills combined with hands-on experience in multimodal LLM evaluation systems.

Required Skills & Experience

• Multimodal LLM evaluation pipeline design
  (LLM-as-a-judge, rubric-based scoring, prompt/few-shot optimization, schema design, agent orchestration)
• Model evaluation engineering
  (golden dataset creation, metric definition: precision/recall/human agreement, A/B testing, benchmarking automation)
• AI serving & inference optimization
  (AWS Bedrock integration, vLLM / GPU inference, latency optimization, cost-aware design, fallback strategies)
• Strong Python backend engineering (~7–10 years)
  (async systems, queues, microservices, service-oriented architecture)
• Trust & Safety / compliance systems
  (rule engines: keyword + context IF-THEN logic, moderation workflows, HITL queues, severity-based escalation)

Nice to Have

• Experience fine-tuning or improving LLM / video understanding models
• Background in content moderation or compliance systems
• Experience with Korean language content processing
  (STT, OCR optimization, NSFW detection pipelines)

Tech Stack

Languages / APIs: Python (FastAPI), React (TypeScript)
LLM / Multimodal: AWS Bedrock, VLMs (e.g. Qwen2.5-Omni), vLLM
Agent frameworks: LangGraph, CrewAI, Google ADK, LangChain
Evaluation: LLM-as-judge, rubric scoring, DeepEval-style eval harnesses
Cloud / Infra: AWS Bedrock, SageMaker, RDS (PostgreSQL), S3, EKS
Observability: Langfuse, prompt testing & tracing tools
Data platform: Databricks
Media processing: Whisper, OCR pipelines, image moderation tools (e.g. AWS Rekognition)

About the project

Our client is a global company with a platform managing over 700,000 hotel rooms across 124 countries, helping clients generate billions in revenue.

Why join

— Stable long-term cooperation: already 5 engineers are working on the project
— Technical freedom: ability to propose architecture decisions and improvements without heavy bureaucracy
— End-to-end ownership: work with the full data pipeline from raw data to production-ready solutions
— Direct business impact: your work helps increase hotel revenues
— Collaboration with major global hotel brands
— Fast and measurable results with clear business value

Requirements

Must have

— 5+ years of experience in Data Engineering
— 3+ years of commercial experience with AWS (including at least 1 year on the latest project)
— 2+ years of experience with Spark/PySpark (including at least 1 year on the latest project)
— Strong experience with:

• Spark / PySpark
• AWS S3
• Kafka or Kinesis
• DynamoDB
• PostgreSQL or MySQL
• AWS ECS Fargate

Nice to have

• AWS Glue
• EMR
• AWS Batch
• Flink
• Apache Beam
• Cassandra / ScyllaDB
• AWS Step Functions

Responsibilities

• Implement scaling of the case detection process (scanner)
• Split monolithic API into multiple services
• Emulate Pub/Sub and Cloud Run locally
• Extract and process data using PySpark + S3
• Work with Kafka/Kinesis pipelines (as a plus)
• Write and optimize SQL queries in PostgreSQL/Redshift
• Use DynamoDB as a data source and sink
• Run and debug ETL jobs on AWS Glue or EMR
• Monitor jobs via AWS Console and Spark UI