AI/ML Engineer

Company Description

Welcome to Nomisma, where we engineer tailored technology solutions to meet industry-specific challenges. We pride ourselves on crafting bespoke strategies that align with unique business goals. At Nomisma, we offer services in software development, advanced tech and infrastructure, hardware prototyping to mass production, and IoT solutions. With our customized solutions, industry expertise, and client-centric approach, we strive for your success.

Role Description

This is a full-time on-site role for an AI/ML Engineer at Nomisma Technology located in Chandigarh. As an AI/ML Engineer, you will be responsible for pattern recognition, computer science, neural networks, statistics, and algorithm development. Your day-to-day tasks will involve designing and implementing AI/ML models, analyzing data, and optimizing algorithms to solve complex business challenges.

Key Responsibilities:

• Develop and implement machine learning models using Python libraries and MATLAB, with a focus on signal and audio data analysis.
• Apply advanced statistical techniques and transformations, such as Wavelet Transforms and Short-Time Fourier Transform (STFT), to analyze time-series data.
• Collaborate with the development team to integrate AI/ML models into broader systems.
• Document all phases of research and development.
• Participate in team meetings and contribute to project planning and review sessions.

Skills and Qualifications:

• Bachelors degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Strong hands-on experience with Python libraries such as TensorFlow, Cuda-DNN, PyTorch, Scikit-learn, AI Studio, and MATLAB.
• Experience in signal processing or audio processing, particularly using statistical methods like Wavelet Transforms, STFT for speech-to-speech applications.
• Familiarity with large language models (LLMs) and their applications.
• Proficient in data structuring and complex algorithms.
• Excellent problem-solving skills and ability to work on complex projects with cross-functional teams.
• Demonstrated statistical acumen in applying statistical methods to experimental and real-world data.