Applied AIML Associate Senior
Qualifications & Requirements
Experience Level: Senior Level
Full Job Description
Embark on a rewarding career advancement as a Senior Machine Learning Engineer within our cutting-edge, AI-driven Surveillance platform at JPMorgan Chase. We are developing a next-generation system designed to proactively detect regulatory violations, insider risks, misconduct, and behavioral anomalies across enterprise communication and collaboration channels.
In this senior role, you will be instrumental in designing, building, and deploying scalable ML and LLM-powered detection systems. Your work will bridge the domains of Risk modeling, Natural Language Processing (NLP), transformer architectures, near real-time inference, and regulatory explainability. This is a hands-on position requiring deep expertise in applied NLP, LLM integration, scalable ML systems, and robust production engineering practices. You will collaborate with product managers, architects, data science teams, and operational groups, while also engaging with the broader software engineering community to explore emerging technologies.
Key Responsibilities
- Design and implement LLM-powered features for risk detection, alert explanation, conversation summarization, and reviewer co-pilots.
- Integrate explainability techniques (e.g., SHAP, LIME, attention visualization) to ensure model outputs are traceable, versioned, and reproducible.
- Optimize inference latency and token efficiency for high-performance production environments.
- Develop and implement Retrieval-Augmented Generation (RAG) and LLM-based risk analysis pipelines capable of processing web-scale data.
- Incorporate data augmentation mechanisms, leveraging legacy regular expressions for filtering and optimization.
- Design and implement real-time and batch processing and scoring pipelines using technologies like Kafka and Spark.
- Establish and maintain experiment tracking, model versioning, and Continuous Integration/Continuous Deployment (CI/CD) pipelines for ML workflows.
- Implement monitoring solutions to detect and alert on data drift, model bias, and performance degradation.
- Collaborate within a cross-functional team following agile methodologies.
- Partner closely with Product Managers, Site Reliability Engineers (SREs), and Compliance Subject Matter Experts (SMEs) to enhance product adoption, reliability, and outcomes.
Required Qualifications
- A minimum of 8 years of experience in cloud-based application development, with at least 4 years specifically as a Machine Learning Engineer.
- A strong foundational understanding of Information Retrieval and Natural Language Processing (NLP).
- Expertise in functional programming and JVM-based languages such as Python, Kotlin, or Java.
- Proven experience integrating machine learning models into cloud-scale, microservices-based architectures.
- Hands-on experience with one or more ML frameworks, including PyTorch, TensorFlow, SciKit, NeMo, or Huggingface Transformers.
- Proficiency with AWS services such as SageMaker, ECS, Lambda functions, and Bedrock.
- Experience with SQL, NoSQL databases, and messaging stacks.
- Exceptional verbal and written communication skills, with a proactive approach and a strong sense of ownership, particularly in early-stage environments.
- Demonstrated operational experience supporting enterprise-grade ML applications in production.
Preferred Qualifications
- Familiarity with Databricks is advantageous.
- Experience with MLOps frameworks like MLflow or Kubeflow is a plus.
- Prior experience in surveillance, fraud detection, fintech, or risk systems is highly desirable.
Company
JPMorganChase
JPMorgan Chase, a venerable financial institution with over two centuries of history, provides innovative financial solutions to a diverse clientele, including consumers, small businesses, and promin...