Staff Machine Learning Engineer, Document & Vision Intelligence

Design and implement machine learning models, services, and components for business problems Write production-grade code for ML models as services and APIs Collaborate with product, data engineering, software development, and business teams Build and maintain scalable data processing workflows and model deployment infrastructure Debug model performance issues, track metrics, and implement continuous improvements Apply modern ML, generative AI, LLM, agentic workflow, and AI engineering tooling Lead complex machine learning solutions across business units Architect infrastructure for automated model training, hyperparameter tuning, and deployment Mentor and guide junior engineers Own end-to-end systems for model monitoring, maintenance, and retraining Requirements B.S. in computer science, computer engineering, electrical engineering, machine learning, statistics, mathematics, or a related quantitative field 6+ years of experience applying machine learning techniques such as ensemble learning, deep learning, reinforcement learning, NLP, generative AI, or related approaches Direct experience designing, building, evaluating, and deploying production-grade ML systems, including model experimentation, evaluation, monitoring, and continuous improvement 6+ years of experience with SQL, Spark or equivalent distributed data processing tools, Python, and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn 4+ years of experience with cloud platforms and environments such as AWS, Microsoft Azure, Databricks and/or Snowflake, and Kubernetes 4+ years of experience applying machine learning techniques in a production environment for business solutions Strong foundation in supervised and unsupervised learning, deep learning, generative AI, and modern AI engineering practices Proficiency in statistical modeling, probability theory, and hypothesis testing Proficiency in Python and experience with TensorFlow, Keras, and PyTorch Familiarity with CI/CD pipelines, Docker, and Kubernetes Deep understanding of MLOps, model versioning, A/B testing, and continuous deployment Deep understanding of Azure, AWS, or GCP, distributed systems, Spark, and Kafka Proven experience leading machine learning projects, managing stakeholders, and scaling ML solutions in production Excellent communication skills for technical and non-technical audiences Exceptional problem-solving and analytical skills Strong product and business acumen AI-native mindset and ability to leverage LLMs, agents, and modern AI tooling Core Competencies Demonstrates expertise in designing and implementing machine learning models and services, with a strong foundation in Python, TensorFlow, and cloud platforms like AWS and Azure. Proven ability to lead complex ML projects, mentor junior engineers, and apply modern AI techniques for business solutions. Highest-signal resume keywords Machine Learning Model Development Production-Grade Code Implementation Cloud Platforms (AWS, Azure) MLOps and CI/CD Pipelines Statistical Modeling and Analysis ATS Optimization Keywords Hard Skills Machine Learning Techniques Python Programming TensorFlow PyTorch SQL Spark Deep Learning Reinforcement Learning NLP Generative AI Soft Skills Excellent Communication Skills Problem-Solving Skills Analytical Skills Business Acumen Mentoring and Leadership Industry Keywords MLOps Model Monitoring Hyperparameter Tuning A/B Testing Continuous Deployment Tools & Technologies Kubernetes Docker Databricks Snowflake Kafka #J-18808-Ljbffr Jobtailor