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.
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