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 recent 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

📌 Applied AI ML Lead (Bengaluru)
🏢 JP Morgan Chase u0026
📍 Bengaluru