Machine Learning Engineer

STAFFXPERT LLC is seeking a Machine Learning Engineer to build and deploy machine learning solutions for data-driven products and business decisions. The role focuses on end-to-end ML pipelines, model optimization, scalable cloud deployment, and collaboration across technical and business teams.

Responsibilities:

• Design, build, and deploy machine learning models for production applications
• Develop and maintain scalable data pipelines for model training, validation, and inference
• Collaborate with data scientists, software engineers, and business stakeholders to translate requirements into ML solutions
• Fine-tune, optimize, and evaluate machine learning and deep learning models
• Implement feature engineering, model monitoring, and performance tracking processes
• Build and maintain MLOps workflows for model deployment, versioning, and retraining
• Work with structured and unstructured data to improve model accuracy and business outcomes
• Perform experimentation, A/B testing, and model validation
• Ensure solutions meet scalability, reliability, security, and performance requirements
• Stay current with advancements in machine learning, AI, and cloud technologies

Requirements:

• Strong programming experience in Python
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn
• Strong understanding of supervised and unsupervised learning techniques
• Experience with data preprocessing, feature engineering, and model evaluation
• Knowledge of SQL and working with large-scale datasets
• Experience deploying ML solutions in cloud environments such as AWS, Azure, or GCP
• Familiarity with Docker, Kubernetes, and CI/CD pipelines
• Understanding of software engineering best practices and version control systems (Git)
• Strong problem-solving and analytical skills
• Experience with LLMs, Generative AI, RAG, Agents, or NLP applications
• Hands-on experience with model fine-tuning techniques such as LoRA, PEFT, or RLHF
• Experience building and maintaining MLOps platforms
• Knowledge of distributed training and large-scale ML systems
• Experience with vector databases, embeddings, and semantic search
• Cloud certifications are a plus