AI Engineer

募集要項

仕事内容

• GenAI Research & Prototyping: Continuously monitor and evaluate new generative AI models and APIs.
• Rapid Proofs-of-Concept: Spin up quick prototypes to benchmark capabilities, latency, cost, and usability.
• Multi-Agent Systems: Design and develop multi-agent architectures to support collaborative AI workflows.
• Fine-Tuning & Domain Adaptation: Prepare domain-specific datasets and fine-tune LLMs (LoRA and full-model) with experiment tracking via MLflow.
• API Integration & Deployment: Design, develop, and maintain secure, scalable integrations with generative AI endpoints (OpenAI, Anthropic, HuggingFace); embed GenAI features into web, mobile, and backend systems.
• Production Support & Training: Provide hands-on support to production teams and deliver training sessions on technical AI concepts.
• Performance Monitoring & Optimization: Define and track key metrics (generation quality, response time, cost per call); implement retrieval-augmented generation (RAG) using scalable vector databases relevance.
• Collaboration & Knowledge Sharing: Partner with product managers, frontend/backend engineers, and data scientists to translate requirements into GenAI solutions; document and follow best practices.
• Stakeholder Communication: Convey complex AI concepts clearly to non-technical audiences.

応募条件

【必須事項】

• Education: Bachelor’s degree in Computer Science or a related field
• Experience: 3+ years of machine learning &or software engineering, with a focus on designing, building, and deploying highly scalable AI solutions
• Programming & Automation: Expert proficiency in Python for API development, automation, and scripting
• GenAI Frameworks & APIs: Hands-on experience with generative-AI APIs (OpenAI, Anthropic, Cohere) and orchestration frameworks (LangChain, LangGraph)
• Fine-Tuning & Experiment Tracking: Skilled in fine-tuning workflows (LoRA and full-model) and experiment tracking tools such as MLflow (or equivalent)
• Model Optimization: Knowledge of model quantization techniques to reduce inference cost and latency
• Retrieval-Augmented Generation: Familiarity with vector databases (and RAG) approaches for improved relevance
• Infrastructure & Deployment: Solid understanding of RESTful and GraphQL services, containerization (Docker), orchestration (Kubernetes), and cloud platforms (GCP, Azure)
• Data Safety & Cost Efficiency: Detail-oriented approach to data privacy, security, and cost optimization
• Collaboration & Communication: Excellent problem-solving skills, clear technical communication, and proven ability to work cross-functionally with product, engineering, and data science teams

【歓迎経験】

• ETL & Data Pipelines: Familiarity with end-to-end ETL processes and tools for data extraction, transformation, and loading
• Scalable ML & GenAI Projects: Proven track record delivering large-scale machine learning or generative-AI applications
• Monitoring & Logging: Solid understanding of application monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack)
• Databricks: Hands-on experience with Databricks for data engineering and ML workflow orchestration
• MCP (Model Context Protocol): Understanding of MCP for managing model inputs, outputs, and conversational context
• Multi-Agent System Orchestration: Experience designing and managing orchestration of multi-agent AI systems
• Real-Time Connectivity: Expertise integrating real-time data streams and low-latency connectivity in production systems

【免許・資格】

【勤務開始日】

応相談

学歴

大学卒以上

雇用形態

正社員

試用期間

試用期間原則なし

勤務地

転勤の有無

転勤なし

受動喫煙防止措置

屋内禁煙

勤務時間

フレックスタイム制
フルフレックス（コアタイムなし）
8:45～17:45（月～木）、8:45～16:00（金）、企画業務型　裁量労働制
裁量労働制の場合、所定労働時間を働いたものとみなす

勤務開始日

応相談

休日休暇

完全週休2日制
年間有給休暇：　
法定休暇：年次有給休暇、産前産後休暇、介護休暇
特別休暇：夏季休暇、年末年始休暇
その他休暇：祝日、5月1日　育児休業制度

年収・給与

年収　　800万円～　経験により応相談

諸手当

裁量労働手当、住宅手当、通勤手当　等

昇給

あり

賞与

あり

採用人数

若干名

待遇・福利厚生

通勤手当：有
寮・社宅：有
住宅手当：有

各種制度

雇用保険：有
労災保険：有
厚生年金：有

選考プロセス

１）書類選考
２）一次面接
３）二次面接
４）最終面接