Applied AI ML Lead

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 Applied AI ML Lead within Corporate Technology team, you will be responsible to 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.

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 JVM based languages- Python/Kotlin, Java Experience integrating models into cloud scale, microservices based architectures 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 Good understanding of data engineering concepts, distributed systems, and scalable architectures Experience working with NLP, LLMs, embeddings, RAG, or GenAI applications Operational experience in supporting an enterprise grade ML application in production Preferred qualifications, capabilities, and skills Knowledge of Databricks is nice to have Experience with any of the MLOps frameworks such MLflow, Kubeflow Experience in surveillance, fraud detection, fintech or risk systems is a strong plus Experience building production-grade ML pipelines and APIs Familiarity with vector databases, model serving, and inference optimization is a plus