Interesting Job Opportunity: Machine Learning Engineer

Hitya Global; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Hitya Global seeks an experienced ML Engineer to build and ship production models for repayment-likelihood scoring, negotiation outcomes, and credit-risk signals. You will own the full model lifecycle from data collection to monitoring.

You will fine-tune LLMs and traditional models, create robust feature pipelines, and ensure reliable model serving with batching, quantisation, and rollback. Collaboration with the AI team is essential for quality, governance, and safety.

Qualifications

• 3+ years building and shipping ML models in production.
• Hands-on experience training and fine-tuning models (PyTorch or TensorFlow).
• Strong feature engineering and data pipeline experience on structured data.
• Experience with model-serving frameworks and inference optimisation.

Responsibilities

• Build and train proprietary models on data for repayment-likelihood scoring and credit-risk signals.
• Own the full model lifecycle: data collection, feature engineering, training, validation, deployment, and monitoring.
• Fine-tune LLMs and smaller models for domain-specific tasks and negotiation dialogue quality.
• Build and maintain evaluation framework to catch model quality regressions.
• Design and operate model serving with batching, quantisation, versioning, and rollback.
• Monitor for drift, degradation, and bias; manage retraining loop.
• Collaborate with AI engineering team on model development and live deployment.

Skills

Model training

Feature engineering

MLOps & CI/CD

Production ML

Tools

PyTorch

TensorFlow

Triton

TorchServe

MLflow

Kubeflow

SageMaker

Job description

Responsibilities

• Build and train proprietary models on Oolka's data for repayment-likelihood scoring, negotiation outcome prediction, and credit-risk signals.
• Own the full model lifecycle : data collection, feature engineering, training, validation, deployment, and monitoring.
• Fine-tune LLMs and smaller models for domain-specific tasks, structured extraction from credit reports, and negotiation dialogue quality.
• Build and maintain the evaluation framework that catches model quality regressions before they ship.
• Build feature pipelines from credit bureau, transaction, and repayment data.
• Design and operate model serving : batching, quantisation, versioning, and rollback for models you own.
• Monitor for model drift, degradation, and bias in production, and own the retraining loop.
• Partner with the AI engineering team; they own how models get built and improved; they own how models get served in the live product.

Requirements

• 3+ years building and shipping ML models in production, not just integrating third-party AI APIs.
• Hands-on experience training and fine-tuning models (PyTorch or TensorFlow), classical ML and/or LLM fine-tuning.
• Strong feature engineering and data pipeline experience on structured/tabular data.
• Experience with model-serving frameworks (Triton, TorchServe, TensorFlow Serving) and inference optimisation : batching, quantisation, and distillation.
• Familiarity with MLOps tooling, experiment tracking, model registries, and CI/CD for models (MLflow, Kubeflow, SageMaker, or equivalent).

ML-Specific Expertise

• Built and shipped models predicting real-world outcomes (risk, churn, ranking, or similar); credit, lending, or fraud experience is a strong plus.
• Experience with offline and online model evaluation, held-out test sets, A/B testing, and shadow deployment.
• Understanding of LLM fine-tuning approaches (LoRA/PEFT) and when fine-tuning beats prompting.
• Comfortable with the bias, fairness, and explainability bar that comes with models touching credit decisions.
• Has debugged a model quality regression in production and traced it back to a data or training root cause.

(ref:hirist.tech)