Sr. Machine Learning
Role: Sr. Machine Learning
Experience: 8-12 years
Visa: H1B(Preferred), H4EAD, GC, USC
Duration: 12 Months
Only W2
Location: Dallas, TX, 75201
Job description:
Sr. Machine Learning
Overview
The Sr.Machine Learning is a technical leader responsible for designing, building, and maintaining scalable, high-performing machine learning (ML) systems. This role bridges the gap between business needs and technical feasibility, ensuring ML initiatives align with overall company strategy.
Responsibilities:
- Design and develop the architecture for ML platforms and solutions.
- Select and implement appropriate ML algorithms and tools based on business requirements and data characteristics.
- Collaborate with data scientists, data engineers, and software engineers to ensure the successful integration of ML models into production systems.
- Oversee data pipelines and ensure data quality for training and deploying ML models.
- Monitor and evaluate the performance of ML models, identifying opportunities for improvement and retraining.
- Stay up-to-date on the latest advancements in ML research and best practices.
- Develop and implement strategies for managing model bias and ensuring the ethical use of AI.
- Communicate complex technical concepts to both technical and non-technical stakeholders.
- Mentor and guide junior team members on ML architecture best practices.
Qualifications:
- Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).
- Strong understanding of machine learning concepts, algorithms, and frameworks.
- Experience designing and developing scalable, distributed systems.
- Experience with cloud platforms (AWS, Azure, GCP) is a plus.
- Excellent communication and collaboration skills.
- Proven ability to translate business goals into technical requirements.
- Experience working in an Agile development environment is a plus.
Tools and Technologies (possible, depending on the company):
- Python (Scikit-learn, TensorFlow, PyTorch)
- Machine Learning Platforms (AWS SageMaker, Azure Machine Learning, Google AI Platform)
- Cloud Computing Technologies (AWS, Azure, GCP)
- Docker, Kubernetes
- DevOps Tools (Git, Jenkins, CI/CD pipelines).