AI/ML Engineer

Design, develop, train, and deploy machine learning models for business applications. Develop AI/ML solutions using Python and machine learning frameworks such as Scikit-learn, TensorFlow, and PyTorch. Perform data preprocessing, cleaning, transformation, and feature engineering. Implement supervised and unsupervised machine learning algorithms. Evaluate, tune, and optimize machine learning models for accuracy and performance. Develop reusable machine learning pipelines for model training and deployment. Work with structured and unstructured data to identify patterns and generate actionable insights. Collaborate with Data Scientists, Data Engineers, Software Engineers, and business stakeholders. Build APIs and integrate machine learning models with enterprise applications. Implement model validation, testing, monitoring, and performance tracking. Deploy and maintain machine learning models in cloud or enterprise environments. Participate in code reviews and follow software development best practices. Maintain technical documentation related to models, pipelines, APIs, and deployments. Troubleshoot issues related to data, models, pipelines, and production deployments. Stay current with emerging AI, Machine Learning, and Generative AI technologies.

3+ years of professional experience in AI/ML Engineering. Strong programming experience with Python. Strong understanding of machine learning algorithms and concepts. Hands-on experience with Scikit-learn, TensorFlow, and/or PyTorch. Experience with Pandas and NumPy. Strong knowledge of supervised and unsupervised learning. Experience with feature engineering and model evaluation. Knowledge of deep learning and neural networks. Experience with SQL and relational databases. Understanding of REST APIs and application integration. Experience with Git/GitHub and version control. Understanding of the machine learning lifecycle. Knowledge of ML model deployment and monitoring. Knowledge of at least one cloud platform such as AWS, Azure, or Google Cloud. Strong analytical, troubleshooting, and problem-solving skills.

Preferred Skills:

Experience with Generative AI and Large Language Models (LLMs). Knowledge of Natural Language Processing (NLP). Experience with Retrieval-Augmented Generation (RAG). Knowledge of vector databases and embeddings. Experience with MLOps practices and tools. Experience with Docker and Kubernetes. Knowledge of CI/CD pipelines. Experience with MLflow, Kubeflow, or similar ML platforms. Experience working with financial services or banking applications is a plus.