Machine Learning Engineer

We are seeking a highly adaptable, creative, and well‑rounded Machine Learning Engineer to join our team. You will own the end‑to‑end ML lifecycle, from dataset creation and foundational research to building and deploying production‑grade models. If you thrive in an environment where you can quickly iterate, experiment with cutting‑edge techniques, and see your work make a tangible impact, this is the role for you. What You'll Do Novel Solution Development: Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems. Rapid Prototyping & Iteration: Build and manage efficient pipelines for rapid experimentation and hypothesis testing. Experiment Tracking: Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases. Model Deployment: Deploy models into production environments using CI/CD practices and model serving frameworks. Performance Monitoring: Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability. Advanced Model Optimization: Apply modern techniques to optimize models for inference speed, memory footprint, and cost. This includes quantization, pruning, and knowledge distillation. Data Lifecycle Management: Lead efforts in dataset creation, augmentation, and curation to build high‑quality, robust training data. Advanced Architectures: Stay current with and apply state‑of‑the‑art techniques, especially relating to Large Language Models (LLMs). What You'll Bring Proven experience (3+ years) in building, training, and deploying machine learning models in a production environment. Expert‑level proficiency in Python. Experience with modern deep learning frameworks, such as PyTorch. Demonstrable experience with systematic hyperparameter searching and optimization frameworks (e.g., Optuna, Ray Tune). Exceptional organizational skills, with a strong emphasis on reproducible research and methodical experiment tracking. Direct experience with LLMs, including fine‑tuning, prompt engineering, RAG, and efficient inference. Practical experience implementing model optimization techniques like quantization (e.g., bitsandbytes) and pruning. Experience in designing and curating novel datasets from scratch. Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field. Bonus Points (Preferred Qualifications) Familiarity with advanced model architectures like Transformers and Mixtures of Experts (MoE). Contributions to open‑source ML projects or a portfolio of personal projects demonstrating a passion for the field. Strong, hands‑on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus).

• LI‑Hybrid
• J-18808-Ljbffr ExaCare AI