Machine Learning Engineer

CareerUS Solutions is seeking a talented Machine Learning Engineer to join their growing AI team. In this role, you'll design, build, deploy, and optimize machine learning models that power intelligent products and data-driven decision-making.

Responsibilities:

• Design, develop, train, and deploy production-grade machine learning models
• Build scalable ML pipelines for data preprocessing, feature engineering, training, validation, and deployment
• Develop predictive models using supervised, unsupervised, and deep learning techniques
• Optimize model accuracy, latency, scalability, and reliability
• Collaborate with software engineering teams to integrate ML solutions into production applications
• Monitor model performance and implement retraining strategies
• Perform feature engineering and data analysis on structured and unstructured datasets
• Work with cloud-based ML platforms and distributed computing frameworks
• Develop APIs and services for model inference
• Implement MLOps best practices including CI/CD, model versioning, and automated deployment
• Stay current with emerging AI and machine learning technologies
• Document technical solutions and mentor junior engineers when needed

Requirements:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Mathematics, Statistics, or a related field
• 5+ years of professional experience in Machine Learning Engineering or AI development
• Strong programming experience in Python
• Experience developing and deploying machine learning models in production
• Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts
• Experience with large datasets and feature engineering
• Knowledge of model evaluation, optimization, and hyperparameter tuning
• Experience building REST APIs for ML services
• Strong problem-solving and analytical skills
• Excellent communication and collaboration abilities
• Experience with Large Language Models (LLMs) and Generative AI
• Experience working with Retrieval-Augmented Generation (RAG)
• Experience fine-tuning transformer models
• Knowledge of Vector Databases
• Experience deploying AI solutions on cloud platforms
• Familiarity with Kubernetes and containerized ML deployments
• Experience with real-time inference systems
• Contributions to open-source AI or machine learning projects