JPMorgan Chase—Applied AI ML Lead

Job description

Description: We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. We are building a next generation, AI-driven Surveillance platform that detects regulatory violations, insider risk, misconduct, and behavioral anomalies across enterprise communications and collaboration systems. As a Senior MLE on the team, you will design, build and productionize ML and LLM powered detection systems that operate at scale across high-volume communication streams. You will work at the intersection of Risk modeling, NLP and transformer architectures, near real-time inference systems, regulatory explainability and auditability. This is a hands-on senior role requiring deep expertise in applied NLP, LLM integration, scalable ML systems and production grade engineering discipline. This role offers a chance to collaborate with product managers, architects, data science and operational teams, while also engaging in software engineering communities to explore new and emerging technologies. Job responsibilities - Design LLM powered features such as risk detection, alert explanation, conversation summarization, reviewer assisted co-pilots - Implement explainability techniques (SHAP, LIME, attention visualization) ensuring model outputs are traceable, versioned and reproducible - Optimize inference latency and token efficiency for production environments - Implement RAG and LLM based risk analysis pipelines processing data at web scale - Bake in augmentation mechanisms leveraging legacy regular expressions for filtering and optimization - Design real-time and batch processing and scoring pipelines (kafka/spark) - Implement experiment tracking, model versioning and CI/CD for ML - Conduct monitoring to detect and alert drift, bias and performance degradation - Work closely within a cross-functional team following agile based processes - Collaborate closely with Product Managers, SRE and Compliance SMEs to continuously improve product adoption, reliability and outcomes Required qualifications, capabilities, and skills - 8+ years experience in cloud based applications with 4+ years of experience as an MLE - Strong foundation in Information Retrieval, Natural Language Processing and - Expert in functional programming and JVM based languages- Python/Kotlin, Java - Experience integrating models into cloud scale, microservices based architectures - Hands-on Databricks experience is required and must include development of production workloads using Spark, Delta Lake, and Databricks Workflows. - Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers - Hands-on experience with AWS services such as SageMaker, ECS, Lambda functions, Bedrock - Experience/Exposure to SQL, NoSQL and messaging stacks - Excellent verbal & written communication skills and bias for action and ownership in early stage env - Operational experience in supporting an enterprise grade ML application in production Preferred qualifications, capabilities, and skills - Experience with building production-grade ML pipelines, APIs amd MLOps frameworks such MLflow, Kubeflow - Experience in surveillance, fraud detection, fintech or risk systems is a strong plus - Good understanding of data engineering concepts, distributed systems, and scalable architectures - Familiarity with vector databases, model serving, and inference optimization is a plus