At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.

With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).

Title: Generative AI Engineer – NLP & Machine Learning Specialist

1. List of Skills

2. Primary Skills

• Generative AI Model Development
  Design, develop, and deploy state-of-the-art generative AI models, including open-source LLMs, tailored for specific business needs.
• Natural Language Processing (NLP)
  Implement advanced NLP techniques such as text generation, summarization, translation, and sentiment analysis for AI-driven solutions.
• Machine Learning & Deep Learning
  Apply cutting-edge ML and deep learning algorithms to enhance AI model accuracy and efficiency in real-world applications.
• Python Programming for AI Development
  Strong proficiency in Python (or R/Java) for developing and implementing AI models, leveraging frameworks like Hugging Face, OpenAI GPT, spaCy, and NLTK.
• Model Fine-Tuning & Optimization
  Customize pre-trained AI models for domain-specific use cases, optimizing them for performance, scalability, and efficiency.
• AI Model Deployment & MLOps
  Develop, deploy, and maintain AI models in production environments using FastAPI, Django, and MLOps tools such as Docker, Kubernetes, and CI/CD pipelines.
• SRE Monitoring Tools Integration & Usage
  Integrate and develop application-level monitoring using SRE tools (Prometheus, Grafana, ELK, Datadog, New Relic, Splunk, etc.) to ensure observability, reliability, and performance of deployed AI solutions.
• Incident Response Automation & Reliability Engineering
  Implement automated incident response workflows, reliability engineering practices, and participate in on-call rotations to maintain high availability and resilience of AI applications.

3. Secondary Skills

• Large Language Models (LLMs) & Open-Source AI Frameworks
  Experience with LangChain, AutoGen, and other frameworks to build scalable AI solutions that leverage large-scale pre-trained models.
• Data Engineering & Data Processing
  Work with data processing frameworks such as Apache Spark, Pandas, PyTorch, NumPy, Scikit-learn, and TensorFlow to prepare high-quality training datasets.
• Conversational AI & Chatbot Development
  Develop intelligent chatbots and conversational AI applications using NLP techniques, integrating with business applications.
• Cloud AI Platforms (GCP, AWS, Azure)
  Strong knowledge of cloud platforms to deploy and scale AI applications efficiently in cloud environments.
• Alerting & Monitoring Pipeline Development
  Build and maintain alerting pipelines using SRE tools (Opsgenie, PagerDuty, ServiceNow, etc.) for proactive incident detection and resolution.
• Infrastructure as Code for SRE Automation
  Use Terraform, Ansible, and similar tools to automate deployment and monitoring infrastructure for AI applications.
• Open-Source Contributions in AI
  Actively contribute to open-source AI projects, improving and innovating existing LLMs and generative AI technologies.
• AI Ethics & Bias Mitigation
  Awareness of AI fairness, ethical considerations, and techniques to mitigate biases in generative AI models.