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.

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 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