Senior Machine Learning Engineer – Acoustic Fault DetectionJob Overview
We are looking for a Senior Machine Learning Engineer – Acoustic Fault Detection with hands-on experience in machine learning, deep learning, and time-series or sensor data analysis. The ideal candidate will develop and optimize ML models to identify faults, detect anomalies, and classify equipment conditions using acoustic and sensor data.
Candidates with additional experience in Digital Signal Processing (DSP) or backend engineering will be preferred.
Key Responsibilities
- Develop machine learning models for fault detection, anomaly detection, and fault classification using acoustic and sensor data.
- Collaborate with DSP engineers to preprocess acoustic signals and extract meaningful features.
- Build, train, evaluate, and optimize machine learning and deep learning models for real-world applications.
- Analyze field data and continuously improve model accuracy and reliability.
- Develop data pipelines for model training and inference.
- Support the deployment, monitoring, and performance evaluation of production ML models.
- Work closely with engineering, DSP, and other cross-functional teams to deliver practical solutions.
Required Skills and Qualifications
Machine Learning and Deep Learning
- Strong understanding of supervised and unsupervised learning techniques.
- Hands-on experience building deep learning models for acoustic data.
- Experience with feature engineering for time-series and sensor data.
- Knowledge of model evaluation, hyperparameter tuning, and performance optimization.
Programming and Technical Skills
- Strong proficiency in Python.
- Experience with NumPy, Pandas, Scikit-learn, and PyTorch or TensorFlow.
- Understanding of time-series analysis, sensor data processing, and feature extraction.
- Experience developing model training, inference, and deployment pipelines.
Good-to-Have Skills
Digital Signal Processing (DSP)
- Basic understanding of Fast Fourier Transform (FFT), spectrograms, wavelet transforms, and digital filtering.
- Exposure to acoustic or vibration signal processing.
Backend Engineering
- Experience with FastAPI, Flask, or similar backend frameworks.
- Familiarity with SQL/NoSQL databases and data processing pipelines.
Candidates with an aptitude or practical experience in either Digital Signal Processing or Backend Engineering, in addition to Machine Learning, will be preferred.
Preferred Industry Background
Experience in one or more of the following areas is desirable:
- Acoustic or vibration analytics
- Industrial AI/IoT
- Predictive maintenance
- Railways or related engineering applications
Experience and Education
- Experience: 2–6 years in Machine Learning.
- Education: B.Tech, M.Tech, or MS in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Electronics, Signal Processing, or a related field.
- Experience working with audio, vibration, or industrial IoT data is preferred.
What We Are Looking For
- Strong analytical and problem-solving skills.
- A hands-on approach to model development and experimentation.
- Ability to work with noisy, real-world sensor data.
- Ownership of outcomes, including taking ML models beyond experimentation into production.
- Ability to collaborate effectively with cross-functional engineering teams.
Pay: ₹800,000.00 - ₹1,400,000.00 per year
Benefits:
- Flexible schedule
- Provident Fund
- Work from home
Application Question(s):
- How many years have you worked on ML projects involving acoustic, audio, or vibration data?
- Have you used DSP techniques such as FFT, spectrograms, beamforming, etc., in practical projects? Mention the ones you have used.
- What is your Notice Period ?
- What is your current CTC (annual compensation in LPA)?
- What is your expected CTC (annual compensation in LPA)?
Experience:
- Deep Learning: 2 years (Required)
Work Location: In person
