SENIOR TECHNICAL ARCHITECT
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 : 8-10 years of experience in developing Data Science, AI, and ML solutions, with a specific focus on generative AI and LLMs in the MedTech / Healthcare / Life Sciences 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.
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