AI Engineer

Job Summary

Key Responsibilities

Model Development & Implementation

• Assist in designing, developing, and validating machine learning models for business use cases such as anomaly detection, fraud detection, risk scoring, and predictive analytics.
• Support data exploration, model training, testing, and performance evaluation activities.
• Research and experiment with new algorithms, techniques, and AI frameworks under guidance from senior team members.

Data Preparation & Feature Engineering

• Collect, clean, transform, and analyze structured and unstructured datasets.
• Perform feature engineering and data preprocessing to improve model performance.
• Work with data engineering teams to understand and utilize data pipelines effectively.

Model Deployment & Monitoring

• Support deployment of machine learning models into development and production environments.
• Assist in monitoring model performance and identifying issues related to model drift or degradation.
• Participate in model testing, debugging, and optimization activities.

Collaboration & Documentation

• Collaborate with data scientists, engineers, and business stakeholders to understand requirements and deliver AI solutions.
• Document model development processes, assumptions, results, and technical specifications.
• Present findings and insights to team members and stakeholders.

Key Requirements

Technical Skills

• 0–3 years of experience in Machine Learning, Artificial Intelligence, Data Science, or related areas (internships and project experience will also be considered).
• Strong programming skills in Python.
• Familiarity with ML/AI libraries such as Scikit-Learn, TensorFlow, PyTorch, Pandas, and NumPy.
• Basic understanding of SQL and data analysis techniques.
• Exposure to cloud platforms (AWS, Azure, or GCP) is a plus.
• Knowledge of Docker, Kubernetes, or MLOps concepts is desirable but not mandatory.

AI/ML Knowledge

• Good understanding of Machine Learning fundamentals, including supervised and unsupervised learning.
• Familiarity with statistical concepts, model evaluation metrics, and feature engineering.
• Understanding of Generative AI, Large Language Models (LLMs), or NLP concepts is an added advantage.

Educational Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Engineering, Mathematics, or a related field.

Soft Skills

• Strong analytical and problem-solving abilities.
• Eagerness to learn and adapt to new technologies.
• Good communication and collaboration skills.
• Ability to work effectively in a team-oriented environment.