ML OPS - Lead

Iris Software; Gurugram District; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Iris Software in Gurugram is seeking a seasoned AI/ML Lead to design and deploy NLP-enabled surveillance models for the financial domain, analyzing emails, chats, and transcripts to detect compliance signals.

The role requires strong MLOps, model training, and production deployment skills using Databricks and MLflow within highly regulated environments.

Qualifications

• Experience with enterprise NLP solutions and model training pipelines.
• Strong understanding of MLOps workflows and CI/CD for ML pipelines.
• Familiarity with regulated environments and compliance considerations.

Responsibilities

• Develop and deploy AI/ML models for surveillance in finance.
• Build NLP solutions, pipelines, and evaluation frameworks.
• Implement MLOps for model serving, monitoring, and lifecycle management.

Skills

NLP

LLMs

Model Training

Python

Databricks

MLflow

Tools

Databricks

MLflow

Job description

ML OPS - Lead Job Description

Job Summary: We are seeking an experienced AI/ML Engineer with strong expertise in Natural Language Processing (NLP), Large Language Models (LLMs), Databricks, and MLflow to develop and enhance Communication Surveillance solutions for the Banking and Capital Markets domain. The candidate will be responsible for building and deploying AI/ML models that detect potential regulatory breaches, market abuse, insider trading indicators, collusion, information leakage, and employee misconduct across electronic communications including emails, chats, collaboration platforms, and voice transcripts. The ideal candidate should have experience developing enterprise‑grade NLP solutions, model training pipelines, and MLOps frameworks in highly regulated environments.

Key Skills

• Natural Language Processing (NLP)
• Large Language Models (LLMs) and Generative AI
• Model Training, Fine-Tuning, and Evaluation
• Python, PyTorch, TensorFlow
• Databricks (Lakehouse, Delta Lake, Workflows, Model Serving)
• MLflow (Experiment Tracking, Model Registry, Model Deployment & Monitoring)

Mandatory Competencies

• Data & AI - MLOPS - CI/CD (for ML pipelines)
• Data & AI - MLOPS - Machine Learning (ML)
• Data & AI - MLOPS - Model Lifecycle Management
• Data & AI - MLOPS - Monitoring & Observation
• Data & AI - MLOPS - Python
• Data & AI - MLOPS - Model Registry & Experiment Tracking
• Data & AI - MLOPS - Model Deployment & Serving
• Data & AI - MLOPS - Data Pipeline & Feature Management