Machine Learning Engineer

CodelogicX is a pioneering technology company, proudly certified with ISO 9001:2015 and ISO/IEC 27001:2013 standards. We are committed to advancing innovation and delivering state-of-the-art solutions that redefine industry standards. We are seeking a skilled and detail-oriented Core Machine Learning Engineer with a strong foundation in classical machine learning and deep learning techniques. The ideal candidate will have hands-on experience building, evaluating, and optimizing machine learning models, with a focus on core ML concepts rather than generative AI or LLM-based systems.

Roles & Responsibilities**:**

· Design, develop, and deploy machine learning models using core ML techniques. 

· Implement supervised and unsupervised learning algorithms for structured and unstructured data. 

· Work on end-to-end ML pipelines including data preprocessing, feature engineering, model training, and evaluation. 

· Apply deep learning models for tasks such as image classification and object detection. 

· Analyze datasets to extract meaningful insights and improve model performance.

· Perform data cleaning, transformation, and feature selection to enhance model accuracy.

· Evaluate model performance using appropriate metrics and validation techniques.

· Optimize models for performance, scalability, and efficiency.

· Collaborate with cross-functional teams including data engineers and software developers.

· Document experiments, workflows, and model performance results.

Requirements

· Minimum 3 years of hands on experience in core machine learning.

· Technical skills:
Strong proficiency in Scikit-learn, PyTorch, NumPy, Pandas, OpenCV, Matplotlib, EAI Library (or similar ML frameworks)

· Machine Learning Algorithms:

• Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest.
• Unsupervised Learning: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformer Architecture, Object Detection, Image Classification.

· Deep Learning: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformer Architecture, Object Detection, Image Classification

• Core ML Knowledge: Data Cleaning and Preprocessing, Feature Engineering, Model Evaluation Techniques (e.g., cross-validation, metrics analysis).

Benefits

Health insurance
Hybrid working mode
Provident Fund
Parental leave
Yearly Bonus Gratuity

Location: Kolkata