We are seeking a
Machine Learning Engineer
with a strong background in AI/ML
techniques and expertise in computer vision
. The ideal candidate will have experience with model fine-tuning
, working with pre-trained models, and deploying machine learning solutions at scale. You will be responsible for developing, optimizing, and fine-tuning ML models for various AI-powered applications, including image and video analysis, object detection, and more.
Key Responsibilities:
-
Design, build, and fine-tune machine learning models for
computer vision
applications, such as object detection, segmentation, image classification, and scene understanding. -
Leverage pre-trained models (e.g.,
CNNs, transformers, YOLO
, etc.) and perform fine-tuning
to adapt them to specific use cases. -
Optimize model performance by implementing cutting-edge techniques in transfer learning, regularization, and hyperparameter tuning.
-
Work closely with data scientists, engineers, and product teams to deliver AI solutions that meet business requirements.
-
Develop scalable solutions and deployment pipelines to operationalize machine learning models.
-
Maintain a strong focus on the efficiency of models, ensuring high speed and low latency in production environments.
-
Stay up-to-date with the latest trends and technologies in AI, machine learning, and computer vision, applying this knowledge to improve existing solutions.
-
Conduct rigorous evaluation of models using relevant metrics and implement strategies for performance improvement.
-
Troubleshoot and resolve model-related issues in production environments.
Required Qualifications:
-
Bachelor's/Master's/PhD in Computer Science
-
3+ years of experience
in machine learning or deep learning, with a focus on computer vision
. -
Expertise in
model fine-tuning
, transfer learning, and working with pre-trained models (e.g., ResNet, VGG, EfficientNet, etc.). -
Strong proficiency in
AI/ML frameworks
such as TensorFlow, PyTorch, Keras, or similar. -
In-depth understanding of
deep learning architectures
(e.g., CNNs, RNNs, transformers). -
Familiarity with vision-specific tasks such as
image classification
, object detection
, semantic segmentation
, and image generation
. -
Hands-on experience with
training large models
and optimizing them for performance and resource constraints. -
Knowledge of data preprocessing, augmentation, and labeling techniques for vision tasks.
-
Proficiency in programming languages such as
Python, C++,
or similar. -
Strong understanding of model evaluation techniques and metrics for computer vision tasks (e.g.,
mAP, IoU, AUC
). -
Experience with
cloud platforms
(AWS, GCP, Azure) and containerization
technologies like Docker is a plus.