Hiring: W2 Candidates Only Visa: Open to any visa type with valid work authorization in the USA
Summary:
A Machine Learning Engineer is responsible for designing, building, and deploying predictive models and AI-driven solutions that support business objectives. This role focuses on developing scalable machine learning systems, optimizing model performance, and integrating intelligent solutions into production applications.
Key Responsibilities
- Design, develop, and implement machine learning models and AI solutions for real-world business use cases.
- Train, test, and evaluate models using large, complex datasets to ensure accuracy and reliability.
- Collaborate with data scientists, data engineers, and software developers to build end-to-end ML pipelines.
- Optimize algorithms and workflows for performance, scalability, and cost efficiency.
- Deploy machine learning models into production environments using MLOps best practices.
- Monitor model performance, drift, and accuracy; retrain and fine-tune models as needed.
- Build and maintain feature engineering pipelines and data preprocessing workflows.
- Document models, assumptions, architectures, and methodologies for transparency and reproducibility.
- Research, evaluate, and apply emerging machine learning techniques, tools, and frameworks.
- Ensure compliance with ethical AI standards, data privacy policies, and regulatory requirements.
- Integrate ML solutions with enterprise applications, APIs, and cloud platforms.
- Conduct A/B testing and experimentation to validate model effectiveness.
- Troubleshoot model failures and collaborate with engineering teams to resolve production issues.
- Support automation and intelligent decision-making across business processes.
- Provide technical guidance on best practices for developing, deploying, and maintaining ML systems.
- Mentor junior engineers and contribute to team knowledge sharing and innovation.
Qualifications
Bachelor s or Master s degree in Data Science, Computer Science, Artificial Intelligence, or a related field. 3-5 years of hands-on experience in machine learning, AI, or advanced analytics roles. Proficiency in Python, R, and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. Strong foundation in statistics, probability, and linear algebra. Experience working with large datasets and building production-grade ML systems.
Preferred Skills / Duties
- Knowledge of natural language processing (NLP), computer vision, deep learning, or reinforcement learning.
- Experience with cloud-based ML platforms and services (AWS SageMaker, Azure ML, Google AI Platform).
- Familiarity with MLOps tools for model deployment, monitoring, and versioning.
- Ability to translate complex business problems into effective machine learning solutions.
- Strong communication, collaboration, and stakeholder engagement skills.
- Experience with data pipelines, feature stores, and model lifecycle management.
For applications and inquiries, contact: [email protected]