Matrix Marketers - Data Scientist - Artificial Intelligence / Machine Learning

Job Summary :

We are looking for an AI / ML Engineer with 4 years of experience in designing, developing, and deploying machine learning and artificial intelligence solutions. The right candidate will have a solid background in algorithms, data pipelines, and model optimization, along with practical experience in production-level ML Responsibilities :

• Design, build, and deploy scalable machine learning models and AI solutions.
• Work with various teams to gather requirements, understand business challenges, and provide AI-driven insights.
• Preprocess, clean, and analyze large datasets to create reliable models.
• Develop and implement deep learning and NLP-based models for real-world applications.
• Improve model performance and ensure efficient use in production environments.
• Maintain, monitor, and enhance existing ML models.
• Collaborate with data engineers to develop data pipelines and MLOps workflows.
• Research and evaluate new AI / ML frameworks, tools, and techniques to improve solutions.
• Document technical designs, workflows, and Skills & Qualifications :
• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 4+ years of experience as an AI / ML Engineer or in a similar role.
• Strong programming skills in Python (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
• Practical experience with ML algorithms (supervised, unsupervised, reinforcement learning, NLP, computer vision).
• Experience with deep learning architectures (CNNs, RNNs, Transformers, LSTMs, GANs).
• Proficiency in handling large datasets using SQL, Spark, or Hadoop.
• Understanding of MLOps practices (Docker, Kubernetes, MLflow, Airflow, or Kubeflow).
• Familiarity with cloud platforms (AWS, Azure, GCP) for ML / AI services.
• Strong problem-solving and analytical thinking abilities.
• Good communication and teamwork Qualifications (Good to Have) :
• Experience with LLMs (Large Language Models) and fine-tuning.
• Knowledge of vector databases and AI search technologies.
• Exposure to edge AI or embedded ML.
• Experience in deploying ML models through REST APIs or microservices.
• Published research papers or contributions to open-source ML projects.

(ref : hirist.tech)