Entry-Level Machine Learning Engineer in Generative AI [28730]

Role : NLP Data Scientist

Responsibilities: -

Design and develop Machine Learning & Statistical Models (using R & Python), and other data analysis techniques to collect, explore, and extract insights from structured and unstructured electronic health record data.

Build and advocate data-driven solutions that help customers improve clinical outcomes and processes.

Design and build scalable production-ready analytics solutions using a wide array of techniques and methodologies in the field of statistical modeling, machine learning, and other AI technologies to meet the needs of given client engagements.

Key Skills: -

• LLM Experience Azure Cloud services LLMs (ChatGPT,) Langchain NLP, NLTK

Experience: -

• Design, develop, deploy, and improve production-grade near real-time scalable machine learning and statistical predictive models and NLP from near Realtime call transcripts and utterances
• Develop novel algorithms and models using state-of-the-art techniques like deep learning neural networks (auto-encoders, feedforward networks, RNNs/CNNs, etc.) and NLP model architectures and algorithms such as BERT (and derivatives like BioBERT, RoBERTa, ALBERT etc.), BiLSTM, XLNet, T5, ELECTRA, PaLM and more
• Partner with cross-functional teams, understand problems, and identify opportunities where advanced analytics and machine learning techniques can be used to make a significant impact and then design, develop, deploy, and monitor those ML solutions Deployment
• Capture and inform your ML infrastructure decisions using your understanding of ML modelling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
• Design, implement, deploy, and maintain deep learning and ML models using cloud technologies (e.g., Azure Databricks, MLFlows and Azure ML)
• Write production-ready modelling code that can be scaled out to 100 million of calls and millions of users Collaboration
• Promote deep scientific expertise, constant learning, attention to detail, and best practices while always being friendly, humble, and open to challenging any assumptions
• Collaborate with data engineers, machine learning engineers, product managers and capability teams to coordinate timely deployments from conception to release
• Promotes and integrates best practices in data science and adheres to established work standards
• Research new machine learning solutions to complex business problems
• Communicate process, requirements, assumptions and caveats of advanced ML and NLP concepts and deliverables in laymen languages to non-technical business leaders

Educational Qualifications: -

• BS, MS, or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, Economics or related quantitative fields
• 5+ years' experience in designing, developing, and deploying production-grade machine learning solutions (supervised, unsupervised, reinforcement learning), deep learning

About CitiusTech

CitiusTech is a leading provider of digital technology and consulting services to payer, provider, medical technology, and life sciences companies. With over 8,500 healthcare technology professionals worldwide, CitiusTech powers healthcare digital innovation, business transformation and industry-wide convergence for over 140 organizations, through next-generation technologies, solutions, and products. Key focus areas include healthcare interoperability, secure data management, quality and performance analytics, value-based care, patient experience, medical imaging, connected health, payer-provider convergence, care coordination and population health management. CitiusTech’s cutting-edge technology expertise, deep healthcare domain expertise and a strong focus on digital transformation enables healthcare and life sciences companies to reinvent themselves to deliver better outcomes, accelerate growth, drive efficiencies, and ultimately make a meaningful impact to patients.