AI/ML Engineer
Robotics Technologies LLC
Albany, NY, United States
4 days ago
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Role details
Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Compensation
$151,840
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Amazon Web Services
Software Applications
Artificial Neural Networks
Microsoft Azure
Big Data
Software Code Optimization
Computer Programming
Continuous Integration
Data Cleansing
R (Programming Language)
Python (Programming Language)
+29 more
Machine Learning
Natural Language Processing
NumPy
Tensorflow
Azure Machine Learning
Sentiment Analysis
Software Engineering
SQL Databases
Data Processing
Enterprise Software Applications
Feature Engineering
Pytorch
Large Language Models
Google Vertex AI
Deep Learning
Model Validation
Generative AI
Git
Pandas
Matplotlib
Build Management
Scikit Learn
Machine Learning Operations
Restful APIs
Document Classification
Docker
Unsupervised Learning
Jenkins
Microservices
Job description
We are seeking an experienced AI/ML Engineer to design, develop, and deploy artificial intelligence and machine learning solutions. The ideal candidate should have strong programming skills, experience with machine learning algorithms, deep learning, data processing, and model deployment., * Design, develop, and implement machine learning models and AI solutions.
- Build predictive models using supervised and unsupervised learning techniques.
- Develop AI-powered applications using Python and machine learning frameworks.
- Work with large datasets to perform data preprocessing, feature engineering, and model training.
- Implement deep learning models using TensorFlow and PyTorch.
- Develop Natural Language Processing (NLP) solutions for text classification and sentiment analysis.
- Build and deploy generative AI and Large Language Model (LLM) applications.
- Develop and optimize neural networks and predictive algorithms.
- Integrate AI/ML models with REST APIs and enterprise applications.
- Perform model evaluation, validation, and hyperparameter tuning.
- Deploy machine learning models using cloud platforms such as AWS, Azure, or GCP.
- Collaborate with data scientists, data engineers, and software development teams.
- Monitor model performance and improve accuracy and scalability.
Requirements
- Python, SQL, and R.
- Machine Learning and Deep Learning.
- TensorFlow, PyTorch, and Scikit-learn.
- Natural Language Processing (NLP).
- Generative AI and Large Language Models (LLMs).
- Neural Networks and Predictive Analytics.
- Pandas, NumPy, and Matplotlib.
- Feature Engineering and Model Optimization.
- REST APIs and Microservices.
- Docker and Kubernetes.
- AWS SageMaker, Azure ML, or Google Vertex AI.
- Git, Jenkins, and CI/CD pipelines.
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