Applied AIML Associate Senior - Machine Learning Engineer, Surveillance

As a Senior Machine Learning Engineer on our team, you will play a crucial role in designing, building, and productionizing ML and LLM powered detection systems that operate at scale across high-volume communication streams. You will be at the forefront of Risk modeling, NLP, and transformer architectures, working on near real-time inference systems, regulatory explainability, and auditability. This hands-on senior role requires deep expertise in applied NLP, LLM integration, scalable ML systems, and production-grade engineering discipline. You will collaborate with various teams and explore new technologies to drive innovation in the field. **Key Responsibilities:** - Design LLM powered features for risk detection, alert explanation, conversation summarization, and reviewer assisted co-pilots - Implement explainability techniques such as SHAP, LIME, and attention visualization to ensure traceable, versioned, and reproducible model outputs - Optimize inference latency and token efficiency for production environments - Implement RAG and LLM based risk analysis pipelines processing data at web scale - Develop augmentation mechanisms leveraging legacy regular expressions for filtering and optimization - Design real-time and batch processing and scoring pipelines using technologies like Kafka and Spark - Implement experiment tracking, model versioning, and CI/CD for ML models - Conduct monitoring to detect and alert drift, bias, and performance degradation - Collaborate closely within a cross-functional team following agile-based processes **Qualifications Required:** - 8+ years of experience in cloud-based applications with at least 4 years of experience as an MLE - Strong foundation in Information Retrieval, Natural Language Processing - Expertise in functional programming and JVM based languages such as Python, Kotlin, Java - Experience integrating models into cloud-scale, microservices based architectures - Hands-on experience with ML frameworks like Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers - Proficiency with AWS services including SageMaker, ECS, Lambda functions, Bedrock - Exposure to SQL, NoSQL, and messaging stacks - Excellent verbal and written communication skills with a bias for action and ownership in early-stage environments - Operational experience in supporting an enterprise-grade ML application in production **Preferred Qualifications:** - Knowledge of Databricks - Experience with MLOps frameworks like MLflow, Kubeflow - Experience in surveillance, fraud detection, fintech, or risk systems is a strong plus This job provides you with an opportunity to work on cutting-edge technologies in AI-driven Surveillance within a collaborative and innovative environment. You will be part of a team that values diversity, inclusion, and continuous improvement to drive success in the financial industry. As a Senior Machine Learning Engineer on our team, you will play a crucial role in designing, building, and productionizing ML and LLM powered detection systems that operate at scale across high-volume communication streams. You will be at the forefront of Risk modeling, NLP, and transformer architectures, working on near real-time inference systems, regulatory explainability, and auditability. This hands-on senior role requires deep expertise in applied NLP, LLM integration, scalable ML systems, and production-grade engineering discipline. You will collaborate with various teams and explore new technologies to drive innovation in the field. **Key Responsibilities:** - Design LLM powered features for risk detection, alert explanation, conversation summarization, and reviewer assisted co-pilots - Implement explainability techniques such as SHAP, LIME, and attention visualization to ensure traceable, versioned, and reproducible model outputs - Optimize inference latency and token efficiency for production environments - Implement RAG and LLM based risk analysis pipelines processing data at web scale - Develop augmentation mechanisms leveraging legacy regular expressions for filtering and optimization - Design real-time and batch processing and scoring pipelines using technologies like Kafka and Spark - Implement experiment tracking, model versioning, and CI/CD for ML models - Conduct monitoring to detect and alert drift, bias, and performance degradation - Collaborate closely within a cross-functional team following agile-based processes **Qualifications Required:** - 8+ years of experience in cloud-based applications with at least 4 years of experience as an MLE - Strong foundation in Information Retrieval, Natural Language Processing - Expertise in functional programming and JVM based languages such as Python, Kotlin, Java - Experience integrating models into cloud-scale, microservices based architectures - Hands-on experience with ML frameworks like Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers - Proficiency with AWS services including SageMaker, ECS, Lambda functions, Be