Data Scientist / AI-ML Engineer

Advance Sensing; Bengaluru; On-site; INR 1,200,000 - 2,800,000; Full time

Job summary

Advance Sensing Bengaluru is seeking an AI/ML specialist to build and deploy computer vision and industrial anomaly detection solutions for manufacturing.

You will lead data preparation, model training, evaluation, and deployment, optimizing architectures, tuning hyperparameters, and monitoring models in production. The role emphasizes edge AI, dataset annotation, and collaboration with cross-functional teams.

Qualifications

• Strong foundation in AI, ML, and DL with supervised vs. unsupervised concepts.
• Proficiency in Python for data science with large-scale data manipulation.
• Solid knowledge of NumPy, Pandas, Matplotlib, and Scikit-learn.
• Familiarity with OpenCV and core computer vision principles.
• Experience deploying ML models and debugging in production environments.

Responsibilities

• Develop and optimize ML models for image and sensor data in manufacturing.
• Build training pipelines, annotate datasets, augment data, and deploy models.
• Experiment with architectures, tune hyperparameters, and monitor production performance.
• Implement edge AI deployment and model quantization.
• Track experiments, manage model versions, and maintain deployment metrics.

Skills

Python

Deep Learning

Machine Learning

Data Analysis

NumPy

Pandas

Matplotlib

OpenCV

Tools

OpenCV

PyTorch

TensorFlow

Keras

REST APIs

SQL

Job description

Batch: 2024/2025/2026.

About the role:

• Join Advance Sensing to develop and deploy machine learning and artificial intelligence systems for industrial applications, with a focus on computer vision, defect detection, and automated quality inspection in manufacturing environments.
• You will work on building, training, and optimizing models to analyze images and sensor data from production lines, identifying anomalies and quality issues at scale.
• Your work spans the full lifecycle from data preparation through model deployment, including experimenting with architectures, tuning hyperparameters, and ensuring models perform reliably in production.

What you'll work on:

• Computer vision and image processing tasks using OpenCV and deep learning frameworks to solve real-world manufacturing challenges.
• Classification, regression, clustering, and object detection workflows tailored to industrial inspection and quality assurance.
• Convolutional neural networks and image-analysis architectures for defect detection, including semantic and instance segmentation for pixel-level identification of manufacturing faults.
• Anomaly detection systems that flag previously unseen or rare defects that traditional methods might miss.
• Time-series analysis and predictive analytics on industrial sensor data to forecast equipment issues or process drift.
• Model optimization, quantization, and deployment strategies for edge AI systems that run on resource-constrained hardware in manufacturing plants.
• Transfer learning approaches, leveraging pretrained models and fine-tuning them for your specific inspection tasks.
• Data pipeline work, including dataset annotation, class balancing, image augmentation, and API integrations to feed models with production data.
• Experiment tracking, model versioning, and deployment monitoring to maintain model quality over time.

What we’re looking for:

• Strong foundation in AI, Machine Learning, and Deep Learning fundamentals, with the ability to explain concepts like supervised versus unsupervised learning, overfitting, regularization, and performance metrics.
• Proficiency in Python for data science and engineering, with hands‑on experience manipulating and analyzing data at scale.
• Solid working knowledge of NumPy, Pandas, Matplotlib, and Scikit-learn for data wrangling and visualization.
• Basic competence in PyTorch, TensorFlow, or Keras for building and training neural networks.
• Familiarity with OpenCV and the core principles of image processing and computer vision.
• Strong analytical and mathematical reasoning ability, with excellent debugging and systematic problem-solving skills.
• Demonstrated interest in or passion for industrial AI, smart manufacturing, machine vision, and quality inspection automation.

Preferred experience:

• Production experience with computer vision and industrial image‑processing applications.
• Track record building CNNs for image classification, defect detection, or related tasks.
• Hands‑on work with object detection architectures and their deployment workflows.
• Experience implementing semantic or instance segmentation to isolate defective regions at the pixel level.
• Familiarity with anomaly detection techniques in industrial or manufacturing contexts.
• Portfolio or examples of transfer learning and model fine‑tuning on domain‑specific datasets.
• Experience with dataset labeling, addressing class imbalance, and applying image augmentation strategies.
• Knowledge of model compression and edge deployment, including quantization and optimization for inference.
• Background in time‑series forecasting or predictive analytics for sensor or IoT data.
• Working knowledge of SQL, relational databases, RESTful APIs, and data pipeline design.
• Understanding of MLOps practices, including experiment logging, model registry, continuous integration for ML, and production monitoring.