Sr. ML Engineer – ML & Applied AI

The Gap•Folsom, CA

About The Position

Gap 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.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
10+ years of experience in machine learning, software engineering, or related roles, with significant experience in production ML systems
Strong 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 TensorFlow
Proven experience designing and deploying scalable ML pipelines and services in production
Hands-on experience with model serving frameworks and API development (e.g., FastAPI, Flask)
Strong experience with containerization (Docker) and orchestration platforms such as Kubernetes
Experience working with cloud platforms (GCP, AWS, or Azure) and building cloud-native ML solutions
Deep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retraining
Experience 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

Nice To Haves

Experience building modern applications involving large language models (LLMs) and agent-based AI systems
Experience with LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflows

Responsibilities

Architect and build scalable, production-grade ML systems from experimentation to deployment and lifecycle management
Design and implement end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, and inference
Develop and maintain high-performance model serving systems using APIs (e.g., FastAPI) for real-time and batch inference
Lead the design and implementation of feature stores and reusable feature pipelines across teams
Build and optimize distributed data processing workflows using Spark, Databricks, or similar platforms
Implement and enforce MLOps best practices, including CI/CD pipelines, automated retraining, model versioning, and experiment tracking
Design and manage model monitoring and observability frameworks to track performance, drift, latency, and system health
Drive strategies for model retraining, drift detection, and continuous improvement
Collaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systems
Contribute to the adoption of modern AI capabilities, including LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflows
Ensure high standards of code quality, testing, documentation, and reproducibility