AI/ML Engineer

Key Responsibilities:

• Design, develop, and implement machine learning (ML) models and algorithms with a focus on predictive analytics and deep learning to address complex business problems.
• Collaborate with data scientists, software engineers, and product managers to integrate AI/ML solutions into production systems.
• Preprocess and analyze large datasets using Databricks, Python, and related tools to extract meaningful insights and ensure data quality for model training.
• Develop front-end interfaces for AI-driven applications using ReactJS to deliver seamless user experiences.
• Optimize and fine-tune ML models using PyTorch for performance, scalability, and accuracy.
• Stay up-to-date with the latest advancements in ML, predictive analytics, and deep learning technologies and apply them to enhance our solutions.
• Conduct experiments to evaluate model performance and iterate on improvements.
• Contribute to the development of robust, scalable, and maintainable codebases.
• Document processes, models, and results to ensure transparency and reproducibility.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field (Ph.D. is a plus).
• 3+ years of experience as an ML developer or in a related AI/ML engineering role.
• Must-have: Strong proficiency in Python for machine learning and data science.
• Must-have: Expertise in machine learning (ML) and PyTorch for building and training deep learning models.
• Must-have: Hands-on experience with Databricks for data processing, model development, and deployment.
• Must-have: Proficiency in ReactJS for developing front-end interfaces for AI/ML applications.
• Strong expertise in predictive analytics and deep learning techniques (e.g., CNNs, RNNs, Transformers) and their applications.
• Familiarity with data processing tools (e.g., Pandas, NumPy) and big data technologies (e.g., Hadoop, Spark).
• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) for deploying AI/ML models is a plus.
• Understanding of software engineering principles, including version control (Git) and CI/CD pipelines.
• Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Excellent communication skills to explain complex technical concepts to non-technical stakeholders.

Preferred Skills:

• Experience with natural language processing (NLP), computer vision, or reinforcement learning.
• Familiarity with MLOps practices and tools (e.g., Kubeflow, MLflow).
• Knowledge of containerization technologies (e.g., Docker, Kubernetes).
• Contributions to open-source ML or deep learning projects or publications in predictive analytics or related fields.