JPMorgan Chase is looking to build, launch and scale an AI/ML platform for the firm, integrating cutting-edge technologies like Generative AI, to achieve state-of-the-art throughput for critical models and reduce inference time for new model architectures.
Requirements
- Extensive hands-on experience with ML frameworks (TensorFlow, PyTorch, JAX, scikit-learn, Ray or Spark).
- Extensive experience with a Public Cloud provider (AWS, Azure, GCP) and addressing non-functional requirements such as scalability and cross-region resiliency.
- Strong coding skills and experience in developing large-scale ML systems and ensuring Software Best Practices.
- Experience with prompt engineering and interacting with various LLM vendors and models.
- Proven track record in contributing to and optimizing open-source ML frameworks.
- Expertise in 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.
Responsibilities
- Architects and implements distributed ML infrastructure, including inference, training, scheduling, orchestration, and storage.
- Develops advanced monitoring and management tools for high reliability and scalability.
- Optimizes system performance by identifying and resolving inefficiencies and bottlenecks.
- Collaborates with product teams to deliver tailored, technology-driven solutions.
- Drives the adoption and execution of ML Platform tools across various teams.
- Integrates Generative AI within the ML Platform using state-of-the-art techniques.
- Assist with interviewing top tier talent as well as acting as a mentor to AI systems Engineers, while fostering a culture of continuous learning and innovation.
Other
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Strategic thinker with the ability to craft and drive a technical vision for maximum business impact.
- Demonstrated leadership in working effectively with engineers, data scientists, and ML practitioners.
- Proven ability to identify trade-offs, clarify project ambiguities, and drive decision-making.
- Collaborate with top talent, mentor engineers, and make a real impact in a fast-paced, innovative environment.
