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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

Category

Skills

Strong Expertise

Generative AI Model Development
Natural Language Processing (NLP)
Machine Learning & Deep Learning
Python Programming for AI Development
Model Fine-Tuning & Optimization
AI Model Deployment & MLOps (Docker, Kubernetes, CI/CD)
Data Science & Statistics
SRE Monitoring Tools Integration & Usage
Development for SRE Observability (Prometheus, Grafana, ELK, Datadog, New Relic, Splunk, etc.)
Incident Response Automation & Reliability Engineering

Basic Proficiency

Large Language Models (LLMs) & Open-Source AI Frameworks
Data Engineering & Data Processing (Apache Spark, Pandas, NumPy, PyTorch, Scikit-learn, TensorFlow)
Conversational AI & Chatbot Development (LangChain, AutoGen)
Cloud AI Platforms (GCP, AWS, Azure)
Alerting & Monitoring Pipeline Development (Opsgenie, PagerDuty, ServiceNow, etc.)
Infrastructure as Code (Terraform, Ansible) for SRE Automation

Knowledge Only

Open-Source Contributions in AI
Software Design Principles & Architecture
AI Ethics & Bias Mitigation
SRE Best Practices & Reliability Patterns

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.

Relocation Supported: No

Visa Sponsorship Approved: No

At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.

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