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Project description 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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.