Role Overview

We are seeking an ML Developer with expertise in natural language processing and financial compliance to design, implement, and benchmark deterministic and transformer-based detection models for electronic communication surveillance. This role is central ability to detect market abuse, conduct risk, information barrier breaches, and off-channel evasion across trader communications.

ASIC INFO 283 explicitly warns against reliance on vendor default alert thresholds, requiring licensees to calibrate models to their specific risk profile. You will build the model benchmarking framework that continuously tests and measures detection model effectiveness, enabling to demonstrate to regulators that its surveillance models are tuned, validated, and performing to measurable standards.

Location open: Bangalore / Gurgaon Key Responsibilities

Design and implement deterministic detection models for eComms surveillance: market abuse language, insider information patterns, tipping-off phraseology, information barrier breaches, conduct risk, off-channel evasion, and trade-comms correlation

Develop and fine-tune transformer-based NLP models (BERT, RoBERTa, FinBERT) for context-aware detection beyond simple lexicon matching

Build and maintain a model benchmarking framework with ground truth datasets, precision/recall/F1 measurement, AUC-ROC analysis, and automated weekly benchmark runs

Implement threshold tuning workflows to calibrate alert sensitivity per desk, asset class, and jurisdiction — ensuring compliance with ASIC INFO 283 guidance

Design false positive reduction strategies: contextual filtering,



trader baseline profiling, alert clustering, and analyst feedback loops

Develop false negative detection: red team simulated misconduct, historical replay testing, coverage gap analysis, and cross-model ensemble voting

Prepare communication data for transformer models: tokenisation, sequence formatting with context windowing, label engineering from investigations, and data augmentation

Implement model drift detection using PSI and KL divergence with automated alerts when distributions shift

Build champion-challenger evaluation: shadow-mode deployment with statistical significance testing before production promotion

Deliver model explainability using SHAP/LIME for regulatory audit readiness

Produce monthly model effectiveness scorecards for compliance committee review

Collaborate with the eComms pipeline team to ensure clean, normalised inputs for ML model training and inference

Required Qualifications

5+ years in machine learning engineering, with at least 3 years in NLP/text classification in financial services or compliance

Robust proficiency in Python (scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers)

Hands-on experience fine-tuning pre-trained language models (BERT, RoBERTa, GPT-family) for domain-specific tasks





Experience building model evaluation and benchmarking pipelines with automated metric tracking and drift detection

Understanding of financial services compliance: market abuse, insider trading, front-running, conduct risk

Experience with threshold tuning, FP/FN trade-off analysis, and precision-recall optimisation

Familiarity with model explainability frameworks (SHAP, LIME) and model governance requirements

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or Computational Linguistics

Preferred Qualifications

Experience with surveillance platforms (NICE Actimize, Behavox, Shield FC) and their detection model architectures

Knowledge of ASIC INFO 283 model calibration requirements

Experience with voice analytics: speech-to-text, speaker diarisation, tonality/sentiment analysis

Familiarity with active learning and human-in-the-loop ML workflows

PhD in NLP, Computational Linguistics, or Machine Learning

Technical Skills & Tools

ML frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, spaCy, NLTK

NLP: BERT, RoBERTa, FinBERT, GPT-family, Word2Vec, FastText, sentence-transformers

Data processing: pandas, NumPy, Apache Spark, Dask, polars

MLOps: MLflow, Kubeflow, Azure ML, Weights & Biases, DVC

Model evaluation: SHAP, LIME, AUC-ROC, precision-recall curves, PSI, KL divergence

Infrastructure: Docker, Kubernetes, GPU compute (NVIDIA A100/H100), Azure ML Compute

Databases: PostgreSQL, Elasticsearch, vector databases (Pinecone, Weaviate, pgvector)

📌 Machine Learning Engineer - Communication Surveillance (Bengaluru)
🏢 Accolite
📍 Bengaluru