What are the responsibilities and job description for the AI / ML Architect position at Eateam?
Experience : 10 years.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 MedTechHealthcareLife Sciences domain. Prior experience in identifying new opportunities to optimize the business through analytics, AIML 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 UNIXLinux 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 AIML solutions as a serviceREST API on Cloud or Kubernetes, and proficiency in testing of developed AI components. Responsibilities also include data analysispreprocessing 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. (1.) To design and architect large-scale solutions, ensuring scalability, performance, and security. (2.) To train and develop team so as to ensure that there is an adequate supply of trained Client in the said technology and delivery risks are mitigated. (3.) To continuously upskill with cutting-edge tech to deliver high-quality, future-proof solutions meeting client expectations and industry standards. (4.) To leverage domainortech expertise to gather client needs, deliver solutions, and craft a technology strategy aligned with business goals.
