AI Ml Engineer

Role & responsibilities

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 

Preferred candidate profile

• Bachelors degree in computer science, Information Technology, or a related field (masters 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.
• 8+ years of hardware engineering, software engineering, systems engineering and/or technical project management 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.