Machine Learning Engineer

Experience: 4+ Years

The ideal candidate will have a strong background in applying machine learning to Digital Signal Processing, particularly in audio signal analysis and accelerometer data analysis. This role involves developing innovative ML models, including CNNs, RNNs, and GANs, and leveraging techniques such as LLMs and low-rank adaptation to enhance our product offerings. The successful candidate will also be proficient in MLOps practices and deploying ML models into production environments.

Key Responsibilities:
●  Custom ML Model Development: Design and build custom machine learning models from scratch, tailored to specific applications in digital and audio signal processing and accelerometer data analysis. The ideal candidate will have published papers or demonstrable customized models custom-built for specific problem domains.
●  Advanced ML Techniques: Apply advanced machine learning techniques, including time series analysis, CNNs, RNNs, GANs, LLMs, and low-rank adaptation, to solve complex problems in pet wellness technology.
●  Data Analysis and Processing: Perform sophisticated data analysis and preprocessing to prepare datasets for machine learning applications.
●  MLOps and Model Deployment: Implement MLOps practices to streamline the deployment of machine learning models into production, ensuring scalability, performance, and reliability.
●  Performance Optimization: Continuously monitor and optimize ML models to improve accuracy and efficiency.
●  Cross-functional Collaboration: Work closely with product development, engineering, and data science teams to integrate ML models into its product ecosystem.
●  Research and Innovation: Stay abreast of the latest developments in machine learning and signal processing to drive innovation within the company.

Qualifications:
● Bachelor's/Master's in Engineering in Computer Science, Data Science, Electrical Engineering, or a related field focusing on machine learning. Preferably from a top-tier (Tier 1 in India/US - IIT, NIT equivalent) institute.
●  Proven experience building custom ML models for digital signal processing, audio signal analysis, and accelerometer data analysis, with a minimum of 4 years of relevant experience.
●  Strong knowledge of time series-based machine learning, CNNs, RNNs, GANs, LLMs, and low-rank adaptation techniques.
●  Experience with MLOps practices and deploying machine learning models in production environments.
●  Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and programming languages (e.g., Python).
●  Excellent analytical, problem-solving, and communication skills.

Preferred Skills:
●  Familiarity with IoT device data processing and analysis.
●  Knowledge of cloud computing platforms and services for ML deployment.