Role Description – Applied AI ML Senior Engineer

Elevate your career as a Machine Learning Engineer, where your technical expertise will shape the future of AI and ML solutions.

As an Applied AI ML Senior Engineer at JPMorgan Chase within the Corporate Sector – AIML Data Platforms, you will contribute to a specialized technical area, driving impact across teams, technologies, and projects. In this role, you will leverage your deep knowledge of machine learning and software engineering to spearhead complex ML projects and initiatives, serving as a catalyst for innovation and solution delivery.

You will be responsible for implementing best practices in ML engineering, with the goal of producing high-quality, scalable ML solutions with operational excellence. You will engage deeply in technical aspects, reviewing code, troubleshooting production ML applications, and enabling new ideas through rapid prototyping. Your passion for parallel distributed computing, big data, cloud engineering, micro-services, automation, and operational excellence will be key.

Job Responsibilities

Collaborate with product teams to deliver tailored, AI/ML-driven technology solutions. Architect and implement distributed AI/ML infrastructure, including inference, training, scheduling, orchestration, and storage. Develop advanced monitoring and management tools for high reliability and scalability in AI/ML systems. Optimize AI/ML system performance by identifying and resolving inefficiencies and bottlenecks. Drive the adoption and execution of AI/ML Platform tools across various teams. Integrate Generative AI and Classical AI within the ML Platform using state-of-the-art techniques. Contribute to the entire AI/ML product life cycle through planning, execution, and future development by continuously adapting, developing new AI/ML products and methodologies, managing risks, and achieving business targets like cost, features, reusability, and reliability to support growth.

Required Qualifications, Capabilities, and Skills

Bachelor's degree or equivalent practical experience in a related field. 5+ years of experience in engineering with a strong technical background in machine learning. Extensive hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, JAX, scikit-learn). Deep expertise in Cloud Engineering (AWS, Azure, GCP) and Distributed Micro-service architecture. Experienced with Kubernetes ecosystem, including EKS, Helm, and custom operators. Background in High Performance Computing, ML Hardware Acceleration (e.g., GPU, TPU, RDMA), or ML for Systems. Strategic thinker with the ability to craft and drive a technical vision for maximum business impact. Demonstrated ability to work effectively with engineers, data scientists, and ML practitioners.

Preferred Qualifications, Capabilities, and Skills

Strong Python coding skills and experience in developing large-scale AI/ML systems. Proven track record in contributing to and optimizing open-source ML frameworks. Understanding & experience of AI/ML Platforms, LLMs, GenAI, and AI Agents.