AI and ML Engineer
About the Role
We are seeking an AI/ML Engineer to design, develop, and deploy machine learning solutions that drive business innovation and deliver measurable impact. This role involves working closely with cross-functional teams to build scalable AI-powered applications, optimize data pipelines, and implement machine learning models that support business and technology objectives.
The ideal candidate combines strong software engineering fundamentals with hands-on experience in machine learning, data analytics, and cloud-based deployment environments. This role offers opportunities to work on cutting-edge AI initiatives while continuously developing technical expertise.
Key Responsibilities
- Design, develop, test, and deploy machine learning models and AI-driven applications for business use cases.
- Build and maintain scalable data processing and ETL pipelines to support model training and inference.
- Collaborate with data scientists, engineers, and business stakeholders to translate requirements into technical solutions.
- Monitor, evaluate, and improve model performance, accuracy, scalability, and reliability
- Implement MLOps best practices, including model versioning, automated deployment, monitoring, and governance.
- Troubleshoot and resolve software, model, and data-related issues to ensure smooth production operations.
- Develop and maintain reusable code, technical documentation, and engineering standards.
- Contribute to continuous improvement initiatives and support innovation through emerging AI/ML technologies and methodologies.
Job Qualifications
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Mathematics, Statistics, or a related field.
- Minimum 1 year of experience in Machine Learning, Artificial Intelligence, Data Engineering, or Software Engineering.
- Proven experience developing and deploying machine learning models in production environments.
- Strong knowledge of Big Data Analytics and data processing frameworks.
- Proficiency in Python and common machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, or similar.
- Understanding of software development lifecycle, version control, testing, and deployment methodologies.
- Strong analytical, problem-solving, and communication skills.
- Experience building and maintaining ETL pipelines.
- Hands-on experience with Machine Learning Operations (MLOps) tools and practices.
- Knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
- Experience with Data Visualization tools such as Power BI, Tableau, or similar platforms.
- Understanding of statistical analysis, predictive modeling, and experimentation techniques.
- Familiarity with generative AI, large language models (LLMs), or AI solution deployment.
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