We are searching for Professionals below business requirements for one of our clients. Please read through the requirements and connect with us in case it suits your profile.
Primary Skills – NLP (Natural Language Processing)
Generative AI & Large Language Models (LLM)
Python Skills
Job Description –
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.