

iBrain Technologies, Inc
AI Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer with a contract length of "unknown" and a pay rate of "unknown." Required skills include proficiency in Python, GenAI experience with AWS, and knowledge of machine learning techniques. Experience in clinical data is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
October 14, 2025
🕒 - Duration
Unknown
-
🏝️ - Location
Remote
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#Data Analysis #Libraries #Project Management #R #SQL (Structured Query Language) #Java #JavaScript #Clustering #NumPy #PyTorch #Athena #ML (Machine Learning) #Jira #Deep Learning #API (Application Programming Interface) #RNN (Recurrent Neural Networks) #S3 (Amazon Simple Storage Service) #Cloud #GIT #Matplotlib #Deployment #Pandas #NLP (Natural Language Processing) #Model Deployment #AI (Artificial Intelligence) #Lambda (AWS Lambda) #SageMaker #AWS S3 (Amazon Simple Storage Service) #Perl #Docker #PySpark #AWS (Amazon Web Services) #Neural Networks #SpaCy #Stories #Python #Hugging Face #Time Series #A/B Testing #TensorFlow #Forecasting #Anomaly Detection #Spark (Apache Spark) #Leadership #SciPy #Databases #Transformers #Programming #Visualization #"ETL (Extract #Transform #Load)" #EC2
Role description
AI Engineer
Remote
Top Skills' Details
1 - Software Development background- Proficiency with Python. Can develop meaningful python code using objective oriented programming and functional programming. Writes tests for code. Can debug errors quickly.
2 - GenAI experience - AWS is primary CSP so candidates need to have experience with bedrock toolsets (80% of time will be spent with GenAI and 20% will be ML focused)
3 - Knowledge of generative AI approaches such as large language models, diffusion models, or GANs is desirable.
4 - Experience with statistical methodologies and machine learning techniques such as: neural networks, graphical models, ensemble methods and natural language processing.
Responsibilities:
Interpret data and present insights through rich and intuitive visualizations that tell compelling stories.
Develop novel ways of integrating, mining, and visualizing diverse, high-dimensional, and poorly curated data sets.
Explore and implement generative AI techniques where appropriate to enhance data analysis capabilities and create new solutions.
Develop and deliver presentations to communicate technical ideas and analytical findings to non-technical partners and senior leadership.
Build underlying software infrastructure to better manage, integrate, and mine data, incorporating both traditional and generative AI approaches.
Work closely with engineering teams and participate in the full development cycle from product inception and research to production deployment.
Write production quality code while implementing both established methods and innovative AI solutions.
Knowledge, Skills & Abilities:
Experience in artificial intelligence and statistical learning.
Experience with statistical methodologies and machine learning techniques such as: neural networks, graphical models, ensemble methods and natural language processing.
Experience with multiple deep learning techniques such as CNN, LSTM, RNN, etc., in addition to standard machine learning approaches such as those found in scikit-learn.
Master of evaluation techniques for supervised and unsupervised techniques. Knows to evaluate the quality of data and determine gaps in data or assumptions.
Proficiency with Python. Can develop meaningful python code using objective oriented programming and functional programming. Writes tests for code. Can debug errors quickly.
Strong data visualization skills.
Familiarity with one or more machine learning libraries or frameworks such as: PyTorch, Tensorflow.
Experience with rational and non-structure databases is highly desirable.
Experience using cloud technologies such as AWS with tools such as S3, Lambda, Athena, API Gateway, SageMaker.
Strong foundation in data analysis and statistical learning.
Must be able to provide evidence of relevant research expertise in the form of presentations, software, technical publications, and/or knowledge of applications.
Experience with statistical methodologies and machine learning techniques including neural networks, graphical models, ensemble methods, and natural language processing.
Knowledge of generative AI approaches such as large language models, diffusion models, or GANs is desirable.
Proficiency with Python and R.
Strong data visualization skills.
Familiarity with machine learning libraries such as PyTorch, TensorFlow, and scikit-learn.
Programming experience in Java, Python, or Perl with knowledge of relational and non-structured databases.
Technical proficiency and demonstrated success in scientific creativity, collaboration, and independent thought.
Ability to translate research concepts into practical solutions and prototypes.
Comfortable working with both technical and non-technical staff.
Strong project management skills with the ability to measure success metrics.
Leadership experience in technical discussions with senior stakeholders.
Must have ability to communicate effectively.
Technical Proficiencies:
Languages: Python , R, SQL, Excel, Java, JavaScript, Spark (Pyspark)
Packages: Scikit-learn, Pandas, NumPy, SciPy, TensorFlow, PyTorch, SpaCy, Hugging Face Transformers, Snorkel, H2O, Spark MLlib, Matplotlib, Seaborn, Statsmodels
Cloud: AWS (S3, Athena, Glue, EC2, SageMaker, Lambda, etc.), or equivalent cloud platforms
Technologies: Git, Jira, Docker
Techniques: Both traditional ML (Random Forest, XGBoost, clustering, etc.) and generative approaches (transformer models, diffusion models, GANs) as appropriate for the problem at hand, Machine learning and deep learning fundamentals, Natural language processing and understanding, Computer vision and image analysis, Exploratory data analysis and feature engineering, A/B testing and experimental design, Time series forecasting and anomaly detection, MLOps and model deployment practices, Ethical AI and responsible model development, Experience in Clinical data preferred
Please send your resume to arunima@ibrain-tech.com
AI Engineer
Remote
Top Skills' Details
1 - Software Development background- Proficiency with Python. Can develop meaningful python code using objective oriented programming and functional programming. Writes tests for code. Can debug errors quickly.
2 - GenAI experience - AWS is primary CSP so candidates need to have experience with bedrock toolsets (80% of time will be spent with GenAI and 20% will be ML focused)
3 - Knowledge of generative AI approaches such as large language models, diffusion models, or GANs is desirable.
4 - Experience with statistical methodologies and machine learning techniques such as: neural networks, graphical models, ensemble methods and natural language processing.
Responsibilities:
Interpret data and present insights through rich and intuitive visualizations that tell compelling stories.
Develop novel ways of integrating, mining, and visualizing diverse, high-dimensional, and poorly curated data sets.
Explore and implement generative AI techniques where appropriate to enhance data analysis capabilities and create new solutions.
Develop and deliver presentations to communicate technical ideas and analytical findings to non-technical partners and senior leadership.
Build underlying software infrastructure to better manage, integrate, and mine data, incorporating both traditional and generative AI approaches.
Work closely with engineering teams and participate in the full development cycle from product inception and research to production deployment.
Write production quality code while implementing both established methods and innovative AI solutions.
Knowledge, Skills & Abilities:
Experience in artificial intelligence and statistical learning.
Experience with statistical methodologies and machine learning techniques such as: neural networks, graphical models, ensemble methods and natural language processing.
Experience with multiple deep learning techniques such as CNN, LSTM, RNN, etc., in addition to standard machine learning approaches such as those found in scikit-learn.
Master of evaluation techniques for supervised and unsupervised techniques. Knows to evaluate the quality of data and determine gaps in data or assumptions.
Proficiency with Python. Can develop meaningful python code using objective oriented programming and functional programming. Writes tests for code. Can debug errors quickly.
Strong data visualization skills.
Familiarity with one or more machine learning libraries or frameworks such as: PyTorch, Tensorflow.
Experience with rational and non-structure databases is highly desirable.
Experience using cloud technologies such as AWS with tools such as S3, Lambda, Athena, API Gateway, SageMaker.
Strong foundation in data analysis and statistical learning.
Must be able to provide evidence of relevant research expertise in the form of presentations, software, technical publications, and/or knowledge of applications.
Experience with statistical methodologies and machine learning techniques including neural networks, graphical models, ensemble methods, and natural language processing.
Knowledge of generative AI approaches such as large language models, diffusion models, or GANs is desirable.
Proficiency with Python and R.
Strong data visualization skills.
Familiarity with machine learning libraries such as PyTorch, TensorFlow, and scikit-learn.
Programming experience in Java, Python, or Perl with knowledge of relational and non-structured databases.
Technical proficiency and demonstrated success in scientific creativity, collaboration, and independent thought.
Ability to translate research concepts into practical solutions and prototypes.
Comfortable working with both technical and non-technical staff.
Strong project management skills with the ability to measure success metrics.
Leadership experience in technical discussions with senior stakeholders.
Must have ability to communicate effectively.
Technical Proficiencies:
Languages: Python , R, SQL, Excel, Java, JavaScript, Spark (Pyspark)
Packages: Scikit-learn, Pandas, NumPy, SciPy, TensorFlow, PyTorch, SpaCy, Hugging Face Transformers, Snorkel, H2O, Spark MLlib, Matplotlib, Seaborn, Statsmodels
Cloud: AWS (S3, Athena, Glue, EC2, SageMaker, Lambda, etc.), or equivalent cloud platforms
Technologies: Git, Jira, Docker
Techniques: Both traditional ML (Random Forest, XGBoost, clustering, etc.) and generative approaches (transformer models, diffusion models, GANs) as appropriate for the problem at hand, Machine learning and deep learning fundamentals, Natural language processing and understanding, Computer vision and image analysis, Exploratory data analysis and feature engineering, A/B testing and experimental design, Time series forecasting and anomaly detection, MLOps and model deployment practices, Ethical AI and responsible model development, Experience in Clinical data preferred
Please send your resume to arunima@ibrain-tech.com