Role: GenAI Architect
Duration: long term
Roles and Responsibilities:
Educational Qualifications:
Graduate or Doctorate degree in information technology, Neuroscience, Business Informatics, Biomedical Engineering, Computer Science, Artificial Intelligence, or a related field.
Specialization in Natural Language Processing is preferred.
Experience Requirements:
* 10 years+ of experience in developing Data Science, AI, and ML solutions, with a specific focus on generative AI and LLMs in the Finance/Telecomm/LSH/Manufacturing/Retail domain.
* Prior experience in identifying new opportunities to optimize the business through analytics, AI/ML and use case prioritization.
* The individual should be a thought leader having a well-balanced analytical business acumen, domain, and technical expertise.
* Large Language Model Expertise: Experience in working with and fine-tuning Large Language Models (LLMs), including the design, optimization of NLP systems, frameworks, and tools.
* Application Development with LLMs: Experience in building scalable applications using LLMs, utilizing frameworks such as LangChain, LlamaIndex, etc and productionizing machine learning and AI models.
* Language Model Development: Utilize off-the-shelf LLM services, such as Azure OpenAI, to integrate LLM capabilities into applications.
* Cloud Computing Expertise: Proven architect kind of experience in cloud computing, particularly with Azure Cloud Services.
* Technical Proficiency: Strong skills in UNIX/Linux environments and command-line tools.
* Programming and ML Skills: Proficiency in Python, with a deep understanding of machine learning algorithms, deep learning, and generative models.
* Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed AI components.
* Responsibilities also include data analysis/preprocessing for training and fine-tuning language models. Also, solves virtually all issues around privacy, real-time, sparce data collection, passive data collection and security and regulatory requirements.