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# Senior Data Scientist / ML Engineer

- **Company:** RIVAGO INFOTECH INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Encodings, Customer Data Management, Information Leak Prevention, Distributed Computing Environment, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Identity and Access Management, Intrusion Detection Systems, Recommender Systems, Reinforcement Learning, QLoRA, Normalized Discounted Cumulative Gain, Pytorch, Transfer Learning, Large Language Models, Apache Spark, Deep Learning, AWS Trainium, Pyspark, Amazon Bedrock, Model Inference
- **Published:** November 3, 2026
- **Apply:** https://www.dice.com/job-detail/07aff06f-b2ee-4957-b6a8-9505e0828bdf

## About the Role

* Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.
 * Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment.
 * LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods).
 * Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.
 * Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch).
 * Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation.
 * Clear written communication with customer and science stakeholders.

Preferred skills

 * AWS Trainium / Inferentia (Neuron SDK).
 * Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.
 * Embedding quantization or semantic IDs.
 * Experience in a customer-facing or embedded role.

## Description

Summary: Works on data to build, train and deploy next-generation LLM-based recommender systems. The role covers the full path from raw customer data to a deployed model., * Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.

 * Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context.
 * Large-scale distributed training on Amazon SageMaker AI, including HyperPod.
 * Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.
 * Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.
 * Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.
 * Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team.
 * Data privacy: handle customer data safely, including removing per