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AI/ML Engineer - Model Dev & Data (Remote - US) at Jobgether. This position is posted by Jobgether on behalf of a partner company. We are currently looking for an . AI/ML Engineer - Model Dev & Data Pipeline. in the . United States. .. This role offers a hands-on opportunity to design, train, and deploy advanced machine learning and AI models at scale. The successful candidate will work closely with cross-functional teams to build production-ready ML pipelines, optimize inference workflows, and fine-tune large language models for domain-specific applications. You will have a direct impact on intelligent systems used by millions of users, contributing to both research-driven innovation and robust production deployments. This position is remote, flexible, and ideal for engineers passionate about AI/ML experimentation, model optimization, and MLOps excellence.. Accountabilities:. Design, train, and fine-tune custom neural networks and large language models using PyTorch, TensorFlow, or JAX.. Implement novel AI/ML architectures from recent research, including attention mechanisms and retrieval-augmented models.. Build and maintain scalable ML pipelines processing high-volume inferences with strict latency requirements.. Develop real-time model serving, vector similarity search systems, and multi-modal embedding solutions.. Establish MLOps workflows for experiment tracking, model versioning, automated training, deployment, and monitoring.. Optimize GPU usage, cloud infrastructure, and cost efficiency for training and inference workloads.. Collaborate with cross-functional teams to ensure models meet business and research objectives.. 7+ years of hands-on experience in ML engineering and production model deployment.. Expert proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).. Deep understanding of transformer architectures, attention mechanisms, and modern NLP.. Experience with large-scale distributed training, model parallelism, and data parallelism.. Strong foundation in statistics, linear algebra, and optimization theory.. Proficiency with MLOps tools such as MLflow, Weights & Biases, Kubeflow, or similar.. Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).. Knowledge of Docker, Kubernetes, and containerized ML workloads.. Hands-on experience with LLM fine-tuning, RLHF, prompt engineering, and multimodal AI models.. Familiarity with retrieval-augmented generation (RAG), vector databases, and model compression techniques.. Preferred: PhD in ML/AI or related field, publications in top AI conferences, experience at AI-first companies or research labs, contributions to open-source ML projects, and knowledge of edge/mobile ML deployment.. Company Location: United States.