
Data Scientist
4 weeks ago
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)-
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