AI / ML Engineer

Technical knowledge on Azure, AWS, Python, Jupyter Notebook, COLAB, ChatGPT, Jurrasic-1, Matplotlib, Sklearn, PyTorch, spacy, HuggingFace, SpeechBrain, Wave2Letter, ElasticSearch, AWS Transcribe, Textract, Amazon Sagemaker, Natural Language Processing (NLP), Voice App Development, Data Structures & Algorithms, Web Development, Machine Learning, Deep Learning, Natural Language Processing, Data Science, Tableau, SAS Programming, SQL for Data Analytics, Power BI, Clinical Trial Analysis & Reporting, Git & GitHub

Roles and Responsibilities:

• Bachelor s degree in computer science, Information Technology, or a related field (master s degree preferred).
• Practical experience in at least one of Java, Python, or JavaScript.
• Practical experience with at least one of Spring, Flask, Django, or Node.js.
• 1+ years of experience.
• Effective communication skills including uplevelling communication for various leadership levels.
• Experience moving technical or engineering programs and products from inception to delivery, articulating the impact using metrics.
• Collaborate with the team in solutioning, design, and code reviews.
• Analytical and problem-solving experience with large-scale systems.
• Experience establishing work relationships across multi-disciplinary teams working remotely.
• Interpersonal skills, including relationship building and collaboration within a diverse, cross-functional team to develop solutions.
• Organizational and coordination skills along with multitasking experience to get things done in an ambiguous and fast paced environment.
• Analytical mindset with the ability to identify and mitigate risks, solve problems, and make data-driven decisions.
• Strong organizational skills and attention to detail, with the ability to manage multiple projects simultaneously.
• Acquire, clean, and preprocess data from various sources to prepare it for analysis and modelling.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies in the data.
• Design and implement data pipelines for efficient data processing and model training.
• Develop and maintain documentation for data processes, datasets, and models.
• Collaborate with data scientists to design and evaluate machine learning models.
• Monitor and assess the performance of machine learning models and make recommendations for improvements.
• Stay up to date with industry trends and best practices in AI/ML and data analysis.