AI Engineer

Delviom LLC
Washington, DC, United States
29 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours

Tech stack

Artificial Intelligence Artificial Neural Networks Data Transformation Data Security Text Processing Python (Programming Language) Machine Learning Natural Language Processing Pattern Recognition Software Engineering Deep Learning AI Platforms
+3 more
Information Technology Machine Learning Operations Unsupervised Learning

Requirements

Bachelor’s degree in Computer Science, Information Technology, Mathematics, Engineering, or a related STEM discipline. Minimum of two years of relevant professional experience in AI, Machine Learning, or related technical domains. Hands-on experience developing AI/ML applications using Python. Practical knowledge of Machine Learning and Deep Learning algorithms, methodologies, and applications. Strong understanding of mathematical and statistical concepts used in AI/ML model development. Experience in data collection, preprocessing, transformation, analysis, and quality management. Experience building AI/ML solutions for automation, predictive analytics, Natural Language Processing, or pattern recognition. Familiarity with the end-to-end AI/ML lifecycle, including model development, testing, validation, deployment, monitoring, and maintenance. Understanding of responsible AI principles, including ethical AI, fairness, bias mitigation, data security, and trustworthy AI practices. Ability to develop and support AI/ML solutions in accordance with established technical, security, and operational standards.

Technologies and Tools Programming Language: Python Artificial Intelligence: AI/ML application development and AI services Machine Learning: Supervised and unsupervised learning techniques Deep Learning: Neural networks and deep learning methodologies Natural Language Processing: Text processing and language-based AI applications

Benefits & conditions

Pattern Recognition: Data-driven pattern identification and classification Predictive Analytics: Forecasting, prediction, and statistical modeling Data Handling: Data preprocessing, transformation, validation, and quality management Frameworks: TensorFlow Model Operations: Model deployment, monitoring, retraining, and lifecycle management (MLOps)

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