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 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