Machine Learning Engineer

Experience: 3–5 Years

Location: Bengaluru (Preferred)

Employment Type: Full-time

About the Role

We are looking for a Machine Learning Engineer who has hands-on experience building, training, deploying, and maintaining production-grade ML models. You will own the complete model lifecycle—from data preparation and feature engineering to model training, evaluation, deployment, monitoring, and retraining. This role focuses on developing proprietary machine learning models that drive credit decisioning, repayment predictions, and AI-powered negotiation systems.

Key Responsibilities

Model Development \& Training

• Build and train proprietary ML models for repayment likelihood scoring, negotiation outcome prediction, and credit risk assessment.
• Own the complete ML lifecycle including data collection, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Fine-tune LLMs and smaller language models for domain-specific tasks such as structured information extraction from credit reports and evaluation of negotiation conversations.
• Design and maintain robust evaluation frameworks to identify and prevent model quality regressions before deployment.
• Develop scalable feature pipelines using credit bureau, transaction, and repayment datasets.
• Implement efficient model serving strategies including batching, quantization, versioning, rollback mechanisms, and inference optimization.
• Monitor production models for drift, degradation, fairness, and bias, and continuously improve model performance.
• Collaborate closely with AI Engineering teams to ensure seamless integration of trained models into production systems.

Required Skills \& Experience

Must-Have

• 3–5 years of experience building, training, and deploying machine learning models in production environments.
• Strong hands-on experience with

PyTorch

and/or

TensorFlow

.

• Experience in classical machine learning as well as LLM fine-tuning techniques.
• Strong expertise in feature engineering and building data pipelines for structured/tabular datasets.
• Experience with model serving frameworks such as

Triton Inference Server

,

TorchServe

, or

TensorFlow Serving

.

• Hands-on experience with inference optimization techniques including batching, quantization, and model distillation.
• Familiarity with MLOps tools such as

MLflow

,

Kubeflow

,

Amazon SageMaker

, or equivalent platforms.

• Experience implementing CI/CD pipelines for machine learning models.

Preferred

• Experience building predictive models for credit risk, lending, fraud detection, customer churn, ranking, or recommendation systems.
• Hands-on experience with offline and online model evaluation techniques including A/B testing, shadow deployments, and held-out validation.
• Strong understanding of LLM fine-tuning approaches such as

LoRA

and

PEFT

, and knowledge of when fine-tuning is preferable to prompt engineering.

• Understanding of model explainability, fairness, and bias mitigation, particularly for credit-related applications.
• Experience diagnosing and resolving production model quality issues caused by data drift or training pipeline changes.

What You'll Get

• Ownership of proprietary machine learning models powering real-world credit decisioning and negotiation systems.
• Opportunity to work on cutting-edge AI products built using India-specific financial and credit datasets.
• Exposure to large-scale production ML systems and end-to-end model lifecycle management.
• Opportunity to shape the future ML platform and grow into a technical leadership role as the product scales.

Preferred Background

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Experience in FinTech, Lending, Banking, Credit Risk, or Financial Services is highly preferred.