End-to-End Machine Learning and Deep Learning Model Development:

Lead the full lifecycle of machine learning projects, from initial data gathering and annotation to deploying models in production.

• Domain Knowledge in Facial Recognition:

Apply expertise in Facial Recognition, including age estimation, profiling & tracking, gender prediction & Person characteristics detection.

• Technical Proficiency:

Demonstrate advanced skills in Python programming, PyTorch, Huggingface, sklearn, pandas, Docker, and REST API development.

• Data Cleaning and Preprocessing:

Perform EDA and data preprocessing and cleaning to prepare datasets for efficient and effective model training.

• Model Selection, Training, and Validation:

Develop and train machine learning and deep learning models, employing SotA techniques and algorithms.

Conduct thorough model selection processes, comparing and evaluating various models to determine the best fit for specific tasks.

• Testing, Benchmarking, and Scaling Models:

Rigorously test models under various scenarios to ensure reliability and robustness. Benchmark model performance against industry standards and scale models to handle large-scale data efficiently.

• Deployment and MLOps:

Deploy machine learning models into production environments, ensuring seamless integration and functionality.

Employ MLOps practices for continuous integration, delivery, and model monitoring in production.

• Technical Documentation:

Create comprehensive documentation for developed models and processes, detailing methodologies, codebases, and user guides.

Ensure clear and understandable documentation for both technical and non-technical audiences, aiding in cross-departmental understanding and collaboration.

• Bachelor’s or master’s degree in computer science, Artificial Intelligence, or Machine
• 3+ years of industry experience with solid coding skills in Python, and experience with Docker, REST APIs, PyTorch, Transformers, sklearn, and other AI/ML frameworks/libraries.
• 3+ years of experience in Facial Recognition
• 3+ years of experience in end-to-end machine learning and deep learning model training on both CPU and GPU servers with parallelism experience
• Strong problem-solving skills with a focus on practical and scalable
• Excellent communication and collaboration abilities to work effectively in a team
• Proactive in staying updated with the latest advancements in machine learning, deep learning, and related technologies.
• Experience withSQL,Elasticsearch, Cloud Services, andPySpark:
  • Leverage SQL and Elasticsearch for data querying and
  • Utilize cloud services and PySpark for distributed computing and large-scale data
• Incremental/Continual ML Model Training:
  • Implement strategies for continual learning and model updating to adapt to new data and evolving requirements.