GenAI + SRE _Assistant Application Developer

Job description

**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. **Location -** Pune,Noida,Chennai,Bengaluru