About the Role

We are looking for an ML Developer with 6+ years of experience to design, build, and deploy machine learning solutions supporting the surveillance function within a financial services setting. You will apply advanced ML techniques — anomaly detection, NLP, behavioural analytics, and classification models — to enhance communication and trade surveillance capabilities, helping detect market abuse, misconduct, and compliance breaches at scale.

Key Responsibilities

- Design, develop, and deploy ML models for surveillance use cases — anomaly detection, behavioural analytics, NLP-based communication analysis, and pattern recognition across large-scale financial data.
- Build and maintain NLP pipelines — text classification, sentiment analysis, entity recognition, and semantic similarity — for electronic communication surveillance.
- Develop and maintain feature engineering pipelines — extracting, transforming, and selecting relevant features from trade, communication, and market data for ML model training.
- Train, evaluate, and optimise ML models — applying supervised, unsupervised, and semi-supervised learning techniques for surveillance detection and alert prioritisation.
- Integrate ML models into surveillance platforms — deploying models as REST APIs or batch inference pipelines for production consumption.
- Implement model monitoring and drift detection — ensuring surveillance models maintain accuracy, reliability, and performance in production.
- Collaborate with surveillance analysts, compliance teams, and data engineers — translating surveillance business requirements into well-designed ML solutions.
- Support alert tuning and false positive reduction — applying ML-driven approaches to improve surveillance detection quality and operational efficiency.




- Produce clear technical documentation — model design documents, feature specifications, and deployment runbooks.

What We're Looking For

1. 6+ years of ML development experience with strong Python and machine learning expertise in production environments.
2. Strong ML skills — supervised, unsupervised, and semi-supervised learning — applied to anomaly detection, classification, and behavioural analytics use cases.
3. Proven NLP expertise — text classification, sentiment analysis, named entity recognition, and semantic search using spaCy, NLTK, Hugging Face, or equivalent.
4. Strong Python proficiency — NumPy, pandas, Scikit-learn, TensorFlow, or PyTorch — for ML model development and data processing.
5. Experience with feature engineering — extracting and transforming features from structured and unstructured financial data for ML model training.
6. Hands-on ML model deployment experience — REST API serving, batch inference, and containerised model deployment using Docker.
7. Experience with MLflow or equivalent — experiment tracking, model versioning, and model registry management.
8. Solid SQL skills — data querying and validation across large-scale financial datasets.
9. Good understanding of financial services compliance or surveillance domain — communication surveillance, trade surveillance, or market abuse detection.
10. Strong AWS experience — SageMaker, S3, Lambda,



or equivalent — for ML model training and deployment.
11. Excellent communication skills — presenting ML findings, model performance, and surveillance insights to compliance and technology stakeholders.

Nice to Have

- Experience with LLMs and GenAI — GPT, Claude, or Llama — for advanced communication surveillance and intelligent alert summarisation.
- Familiarity with surveillance platforms — NICE Actimize, Behavox, or equivalent — and their ML integration patterns.
- Knowledge of graph-based ML — network analysis and entity relationship modelling for market abuse detection.
- Exposure to real-time streaming ML — Kafka or AWS Kinesis — for real-time surveillance alert generation.
- Experience with explainability frameworks — SHAP, LIME — for model transparency and regulatory audit support.
- Knowledge of regulatory frameworks — MiFID II, MAR, ASIC Market Integrity Rules — and their surveillance implications.
- AWS Certified Machine Learning Specialty certification.
- Experience in Financial Services, Banking, or regulated industry environments.

Bounteous x Accolite makes the future faster for the world’s most ambitious brands. Our services span Strategy, Analytics, Digital Engineering, Cloud, Data & AI, Experience Design, and Marketing. We are guided by Co-Innovation, our proven methodology of collaborative partnership. Bounteous x Accolite brings together 5,000+ employees spanning North America, APAC, and EMEA, and partnerships with leading technology providers. Through advanced digital engineering, technology solutions, and data-driven digital experiences, we create exceptional and efficient business impact and help our clients win.

For more information visit: www.accolite.com

📌 ML Developer (Gurugram)
🏢 Accolite Digital India
📍 Gurugram