Junior-Mid Machine Learning Engineer at adidevtechnologies

Setup: Hybrid
Location: Austin, Texas
Type: Full-time
Level: Mid-level

Description

Adidev Technologies Inc. is seeking a Junior-Mid Level Machine Learning Engineer specializing in Generative AI. The role involves architecting and refining ML models, executing data analysis, integrating Generative AI into products, and staying current with ML trends and best practices. This position focuses on transforming innovative ideas into valuable AI-driven solutions.

Required Skills

machine learning
deep learning
NLP
computer vision
reinforcement learning
Generative AI models
GANs
Transformers
SQL/NoSQL databases
data visualization tools
version control systems
algorithmic complexity
data structure optimization
Python
TensorFlow
PyTorch
scikit-learn
cloud computing
big data platforms
AWS
Azure
GCP
containerization
Kubernetes
CI/CD pipelines
MLOps practices

Additional Information

Required Previous Experiences: Experience in machine learning, deep learning, NLP, computer vision, reinforcement learning, and/or other AI domains.
Experience with Generative AI models and frameworks like GANs or Transformers in industry settings.
Practical experience with SQL/NoSQL databases, data visualization tools, and version control systems.
Strong foundational understanding of algorithmic complexity and data structure optimization.
Excellent problem-solving, collaboration, and communication abilities.
Ability to develop and implement cutting-edge machine learning models, with a focus on Generative AI applications like text generation, image synthesis, and creative AI.
Ability to execute comprehensive data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions.
Proficient in staying ahead of AI research, especially in Generative AI, and applying the latest findings and techniques to drive project innovation.
Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
Strong background in cloud computing and big data platforms (AWS, Azure, GCP), with hands-on experience in cloud-based ML services and serverless architectures.
Familiarity with DevOps for AI, including containerization (Docker, Kubernetes), CI/CD pipelines, and MLOps practices.
Ability to facilitate knowledge sharing and best practices in AI/ML, particularly focusing on Generative AI, within the team.
Ensures all AI implementations comply with ethical guidelines and data privacy standards.