GenAI Engineer - Application Developer

Job description

Generative AI Engineer NLP Machine Learning Specialist 3-6 year of Exp. 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 **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) Knowledge Only- Open-Source Contributions in AI- Software Design Principles Architecture - AI Ethics Bias Mitigation **Primary Skills** - Generative AI Model DevelopmentDesign, 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 DevelopmentStrong 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 MLOpsDevelop, deploy, and maintain AI models in production environments using FastAPI, Django, and MLOps tools such as Docker, Kubernetes, and CI/CD pipelines. **Secondary Skills** - Large Language Models (LLMs) Open-Source AI FrameworksExperience with LangChain, AutoGen, and other frameworks to build scalable AI solutions that leverage large-scale pre-trained models. - Data Engineering Data ProcessingWork with data processing frameworks such as Apache Spark, Pandas, PyTorch, NumPy, Scikit-learn, and TensorFlow to prepare high-quality training datasets. - Conversational AI Chatbot DevelopmentDevelop 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.Open-Source Contributions in AIActively 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.