AI Ml Engineer

NOTE: Candidate should be available for the interview on2nd Aug Virtually . Immediate to 30 days notice period only.

Preferred Qualifications:

• Experience in AI/ML in the healthcare sector
• Experience working with cross-functional and distributed teams in a global and diverse environment
• Experience in establishing AI/ML best practices, standards, RUAI, and ethics

Required Qualifications:

• Proven solid Python programming skills and experience with deep learning popular frameworks such as PyTorch
• Proficiency in SQL and other programming languages for data analysis and modeling
• Proficiency with cloud development environments such as Azure, AWS, and GCP
• Proven in-depth knowledge in Generative AI technology and Large Language Models (LLM)
• Experience with LLM frameworks and/or NLP approaches such as text embedding
• Experience working on collaborative software projects using GitHub
• Proven solid technical communication skills
• Experience with both traditional machine learning algorithms and modern deep learning and generative AI models
• Proven good knowledge of GPU use, and the ability to rapidly set up pipelines for testing new ideas
• Proven excellent analytical and problem-solving skills, including the ability to disaggregate issues, identify root causes, and recommend solutions
• Skill Set; NLP, NLU, NLI, Transformers, Attention, GPT, Llama, Mistral, Model Quantization, Model Optimization, Retrieval & Ranking, RAG, RAGAS
• Statistics, Machine Learning Models, Model Deployment
• Proven excellent communication, writing, and presentation skills
• Proven advanced understanding of Deep Learning with applications in Time series Processing, NLP, and multimodal modeling

GenAI Model Utilization and Optimization

• Ability to utilize state-of-the-art GenAI models, understand and explain power and limitation of GenAI models
• Ability to utilize GenAI model for custom solutions, exposure in RAG, LangChain, VectorDBs
• Ability to Quantize, Optimize GenAI models
• Clear understanding and training to handle sensitive data, how to use that safely with GenAI and any other AI models, how deal with restriction and comply with best practices at all time

AI/ML Development and Deployment

• Develop novel AI approaches for healthcare using both structured and unstructured data
• Ability to utilize GenAI models and build and test deep learning and/or traditional ML models in cloud computing environment
• Perform data analysis, pre-processing, and feature engineering required to ensure quality and reliability of the AI models and optimize them for performance and robustness
• Develop and deploy big data pipelines and frameworks for data ingestion, processing, and analysis
• Develop machine learning and deep learning models and systems in domains including, but not limited to: NLP, NLU and multidimensional time series forecasting among others
• Ability to understand document processing, image processing and NLP and bridging the domain knowledge
• Build Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing the ML models

Experimentation and Analysis

• Design and plan custom experiments, conduct statistical analysis and create data visualizations, and present and communicate in standardized format
• Run A/B experiments, gather data, and perform statistical analysis
• Perform hands-on analysis and modeling of healthcare data sets to develop insights that increase business value

Collaboration and Communication

• Communicate findings with business stakeholders and collaborate to develop solutions that meet customer needs
• Collaborate with the Responsible Use of AI (RUAI) team to ensure that the delivered solutions are compliant with company policies and standards
• Collaborate with engineering teams to develop robust data pipelines of MLOps procedures for new solutions

Best Practices and Documentation

• Understand full end-to-end machine learning development process
• Establish best practices for end-to-end deep learning and machine learning development cycle to ensure rigor in process and quality in outcome

Innovation and Research

• Stay up to date with latest technologies and literature
• Help design and develop the next generation of NLP, ML & AI products, and services for healthcare
• Research and implement innovative machine learning model, module and approaches
• Present findings and insights to team members and if required to senior

Project and Technology Management

• Run large complex proof-of-concepts for the healthcare business
• Manage prioritization and technology work for building GenAI, NLP, ML & AI solutions