Generative AI Architect - NLP

Job Description : Role : Gen AI Architect Roles and Responsibility : - Lead the design and architecture of scalable conversational AI systems, ensuring seamless integration with existing platforms. - Develop and optimize NLP, NLU, and NLG models to enhance the accuracy and relevance of AI-driven responses. - Collaborate with cross-functional teams, including software engineers, data scientists, and UX designers, to deliver superior conversational interfaces. - Evaluate and recommend cutting-edge conversational AI frameworks and tools, staying current with industry advancements. - Oversee the training, deployment, and continuous improvement of machine learning models within the conversational AI ecosystem. - Ensure compliance with security standards, data protection regulations, and privacy policies in the development and deployment of AI systems. - Expertise in Python, Java for developing AI models and integrating conversational systems. - Hands-on experience with natural language processing frameworks (e.g., SpaCy, NLTK), understanding of dialogue management systems, and the ability to develop custom models for specific applications. - Extensive experience with TensorFlow, PyTorch, or Keras for building and fine-tuning AI models, including transformer-based architectures like BERT, GPT, or T5. - Strong working knowledge of platforms such as Rasa, Dialog flow, Microsoft Bot Framework, or IBM Watson Assistant for designing, deploying, and managing chatbots and virtual assistants. - Experience in setting up data pipelines, managing large datasets, and implementing MLops practices for continuous integration, delivery, and monitoring of machine learning Architecture Design & Development : - Design and architect advanced generative AI models and systems tailored to specific business needs (e.g., natural language processing, image generation, recommendation systems). - Lead the end-to-end development of generative AI systems, from conceptualization to production deployment, ensuring scalability and performance. - Integrate AI models with existing infrastructure, software systems, and applications. AI Model Training & Fine-Tuning : - Select, fine-tune, and train large-scale generative models (e.g., GPT, BERT, StyleGAN, DALL-E) to meet specific objectives. - Ensure models are trained effectively using high-quality datasets and maintain high standards of model performance. Innovation & Research : - Stay ahead of AI trends, research new developments in generative models, and incorporate the latest advancements into the company's AI strategy. - Lead research initiatives and collaborate with data scientists, engineers, and product teams to drive innovation. Collaboration & Leadership : - Work closely with stakeholders, including data scientists, engineers, business analysts, and product managers, to define requirements and deliver AI-driven solutions. - Provide technical leadership, mentorship, and guidance to junior team members. Ethics & Governance : - Advocate for ethical AI practices, ensuring models are designed with fairness, transparency, and compliance in mind. - Address potential biases and ethical concerns in generative AI systems. Required Qualifications : Education : - Master's or PhD in Computer Science, Artificial Intelligence, Data Science, or a related field. Experience : - Proven experience (X years) in designing, developing, and deploying generative AI systems. - Strong background in deep learning techniques, especially those used in generative models (e.g., GANs, VAEs, transformers). - Hands-on experience with AI frameworks such as TensorFlow, PyTorch, Hugging Face, OpenAI, etc. - Proficient in programming languages such as Python, Java, or similar. - Experience with cloud platforms (AWS, GCP, Azure) for AI model deployment and scaling. Skills : - Expertise in designing large-scale AI architectures, including model deployment and continuous learning pipelines. - Strong understanding of data engineering and MLOps practices. - Excellent problem-solving and critical-thinking abilities. - Strong communication and collaboration skills, with the ability to present complex technical concepts to non-technical stakeholders. Preferred Qualifications : - Experience in NLP, computer vision, or multimodal AI applications. - Familiarity with reinforcement learning, few-shot learning, or self-supervised learning. - Knowledge of AI ethics, privacy concerns, and bias mitigation strategies. (ref:hirist.tech)

Location: in, IN

Posted Date: 2/5/2025