Senior Applied ML Engineer

Location-Remote

Job Summary

We are seeking a Senior Applied ML Engineer to develop, deploy, and optimize machine learning and AI solutions that solve real-world business problems. The ideal candidate will have strong expertise in machine learning, deep learning, data engineering, and MLOps, with experience in deploying scalable AI applications in production.

Key Responsibilities

• Design, build, and deploy machine learning models for production environments.
• Develop AI/ML solutions using supervised, unsupervised, and deep learning techniques.
• Fine-tune and optimize Large Language Models (LLMs) and Generative AI applications.
• Build and maintain end-to-end ML pipelines for data preprocessing, training, evaluation, and deployment.
• Collaborate with data scientists, software engineers, and product teams to deliver AI-powered solutions.
• Implement MLOps practices for model versioning, monitoring, and continuous deployment.
• Evaluate model performance and continuously improve accuracy, scalability, and efficiency.
• Conduct experiments and prototype new AI capabilities using the latest research and technologies.
• Ensure AI solutions meet security, privacy, and governance requirements.
• Mentor junior engineers and contribute to technical best practices.

Required Skills

• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering.
• Knowledge of NLP, computer vision, or recommendation systems.
• Experience with vector databases and embedding models.
• Hands-on experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
• Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Familiarity with Docker, Kubernetes, CI/CD, and Git.
• Strong SQL and data engineering fundamentals.
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Experience deploying scalable AI applications in production.
• Knowledge of distributed computing frameworks such as Spark or Ray.
• Familiarity with AI evaluation, model monitoring, and responsible AI practices.
• Experience with AI agents, multi-agent systems, or workflow orchestration frameworks is a plus.