Sr. ML Engineer - ML & Applied AI

About the RoleGap Inc. is seeking a Senior Machine Learning Engineer with 10+ years of experience to design, build, and scale production-grade machine learning and AI systems that power data-driven decision making across the enterprise.This role is focused on end-to-end ML system ownership, including data pipelines, feature engineering, model training, deployment, monitoring, and continuous optimization. You will lead the development of scalable ML platforms, drive best practices in MLOps, and enable reliable, high-performance model inference in both batch and real-time environments.The ideal candidate combines strong software engineering expertise with deep ML knowledge and has experience building robust, scalable ML systems in production, including modern applications involving large language models (LLMs) and agent-based AI systems.What You'll DoArchitect and build scalable, production-grade ML systems from experimentation to deployment and lifecycle managementDesign and implement end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, and inferenceDevelop and maintain high-performance model serving systems using APIs (e.g., FastAPI) for real-time and batch inferenceLead the design and implementation of feature stores and reusable feature pipelines across teamsBuild and optimize distributed data processing workflows using Spark, Databricks, or similar platformsImplement and enforce MLOps best practices, including CI/CD pipelines, automated retraining, model versioning, and experiment trackingDesign and manage model monitoring and observability frameworks to track performance, drift, latency, and system healthDrive strategies for model retraining, drift detection, and continuous improvementCollaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systemsContribute to the adoption of modern AI capabilities, including LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflowsEnsure high standards of code quality, testing, documentation, and reproducibilityWho You AreBachelor’s or Master’s degree in Computer Science, Engineering, or a related field10+ years of experience in machine learning, software engineering, or related roles, with significant experience in production ML systemsStrong programming expertise in Python and solid software engineering fundamentals (data structures, system design, APIs)Extensive experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlowProven experience designing and deploying scalable ML pipelines and services in productionHands-on experience with model serving frameworks and API development (e.g., FastAPI, Flask)Strong experience with containerization (Docker) and orchestration platforms such as KubernetesExperience working with cloud platforms (GCP, AWS, or Azure) and building cloud-native ML solutionsDeep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retrainingExperience implementing CI/CD pipelines for ML workflows and managing version control systems (Git)Strong experience with SQL and distributed data processing frameworks (e.g., Spark, PySpark)Excellent problem-solving skills and ability to design scalable, maintainable systems #J-18808-Ljbffr