AI Engineer
New
J
JobgetherArtificial intelligence
Work remotely from India, with no regular office attendance required., Standard working hours are 8:00 a.m. to 5:00 p.m. India Standard Time (IST).Full-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3–4 years of relevant hands-on experience in AI engineering, machine learning, data science, or a closely related field.
- Required Skills
- PythonMachine LearningNumpyPyTorchPandasTensorflowDeep Learningscikit-learnNLPLLM
Requirements
- Hold a bachelor's or master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical discipline.
- Have 3–4 years of relevant hands-on experience in AI engineering, machine learning, data science, or a closely related field.
- Understand machine learning algorithms, deep learning fundamentals, and common model development techniques.
- Have practical Python programming experience developing, testing, and maintaining machine learning workflows.
- Have hands-on experience with at least one major AI or machine learning framework, such as PyTorch or TensorFlow.
- Be familiar with Scikit-learn, Pandas, NumPy, and regular expressions for text and data processing.
- Have experience with model training, fine-tuning, experimentation, and performance optimization.
- Understand model evaluation methodologies, performance metrics, and techniques for assessing model accuracy and effectiveness.
- Be familiar with NLP and LLM applications.
- Be able to clean, preprocess, transform, and engineer features from datasets.
- Additional experience with Hugging Face Transformers, spaCy, AWS SageMaker, Google Vertex AI, Azure AI, MLOps, CI/CD, responsible AI, or model integration into applications and API-based services is advantageous.
Responsibilities
- Design, build, train, and fine-tune machine learning models for business challenges and analytical products.
- Clean and preprocess large datasets, perform feature engineering, and prepare training data.
- Build, test, and optimize machine learning and deep learning models using PyTorch, TensorFlow, and Scikit-learn.
- Support the development of NLP, LLM, text analysis, and predictive analytics applications.
- Design experiments, analyze results, and use evaluation metrics to assess model accuracy, reliability, and effectiveness.
- Optimize model efficiency, inference latency, and resource utilization.
- Collaborate with software engineers to deploy models and integrate them into production applications and API-based services.
- Use relevant libraries, frameworks, and cloud-based AI services for experimentation, development, deployment, and model management.
- Contribute to reproducible model development, testing, deployment workflows, and CI/CD practices for machine learning systems.
- Consider bias, transparency, interpretability, and ethical AI principles when developing and evaluating models.
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