Senior AI/ML Engineer (Communication Surveillance)

Title: Senior AI/ML Engineer - Communication Surveillance

Exp: 7-12 Years

Notice Period : Immediate Joiner

We are seeking an experienced AI/ML Engineer to support the development and enhancement of Communication Surveillance solutions for a leading Banking and Capital Markets client. The role focuses on designing, developing, and deploying enterprise-grade AI/ML solutions that identify regulatory breaches, market abuse, insider trading indicators, collusion, information leakage, and employee misconduct across multiple communication channels, including emails, chat platforms, collaboration tools, and voice transcripts.

You will leverage Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, Databricks, and MLflow to build scalable, production-ready machine learning solutions while adhering to the security, compliance, and governance standards required in highly regulated financial environments.

Responsibilities

Design, develop, train, fine-tune, and deploy AI/ML models for Communication Surveillance use cases.

Build advanced NLP solutions for analyzing structured and unstructured communication data.

Develop and optimize Large Language Model (LLM) and Generative AI-based solutions for surveillance and compliance applications.

Create scalable machine learning pipelines using Databricks Lakehouse Platform.

Manage model lifecycle using MLflow, including experiment tracking, model registry, deployment, monitoring, and version control.

Develop data preprocessing, feature engineering, model training, evaluation, and inference pipelines.

Work closely with business stakeholders, compliance teams, and domain experts to translate surveillance requirements into AI solutions.

Ensure model explainability, accuracy, robustness, and regulatory compliance.

Optimize model performance for production deployment and continuous improvement.

Collaborate with data engineers and platform teams to integrate AI solutions into enterprise applications.

Follow MLOps best practices for CI/CD, model governance, monitoring, and production support.

Document technical designs, model architecture, deployment processes, and operational procedures.

Skills

Must have

7-12 years of overall IT experience.

Strong experience in Machine Learning and Deep Learning.

Strong experience in Natural Language Processing (NLP).

Hands-on experience with Large Language Models (LLMs) and Generative AI.

Experience in Model Training, Fine-Tuning, and Evaluation.

Strong programming experience in Python.

Hands-on experience with PyTorch and TensorFlow.

Strong experience with Databricks (Lakehouse, Delta Lake, Workflows, and Model Serving).

Experience with MLflow, including Experiment Tracking, Model Registry, Model Deployment, and Monitoring.

Nice to have

Experience in Banking and Capital Markets domain.

Experience with Communication Surveillance, Trade Surveillance, Financial Crime, or Compliance platforms.

Understanding of regulatory requirements related to market abuse, insider trading, employee misconduct, and communication monitoring.

Experience working with email, chat, voice transcript, and collaboration platform data.

Knowledge of cloud platforms such as Azure or AWS.

Experience with distributed data processing technologies such as Apache Spark.

Familiarity with vector databases, Retrieval-Augmented Generation (RAG), prompt engineering, and agentic AI architectures.

Exposure to model monitoring, drift detection, and production support activities.

Experience working in highly regulated enterprise environments.

Skills: ml,banking domain,machine learning,genai,nlp,surveillance,llm,deep learning,python,databricks