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Role : Senior Data Scientist / AI Engineer

Rivago infotech incUnited States🇺🇸United StatesPosted Sep 16, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
19 hours ago
DockerNeo4jMLOpsMLflowMachine LearningNLPAirflowAzurePyTorchPythonTensorFlow

Job Description

Role : Senior Data Scientist / AI Engineer

Location : Remote

 

IMP: - Skills - Agentic AI (A2A , MCP) , Azure Cloud , healthcare -payer space experience.

 

Exp : 10+

 

Job description:-

We’re looking for a few Senior Data Scientist / AI Engineer with deep expertise in advanced Machine Learning, Large Language Models (LLMs) and Knowledge Graphs,.

This role is ideal for someone who enjoys solving complex problems, building intelligent systems end to end, and driving measurable impact in data rich environments.

 

What You’ll Do:

- Design advanced ML models across NLP, optimization, predictive modeling, and statistical learning.

- Own end to end MLOps pipelines: data ingestion, training, deployment, monitoring, CI/CD.

- Collaborate with engineering, product, and domain teams to deliver production ready AI solutions.

- Build and scale Knowledge Graph–driven AI systems (ontology design, graph embeddings, reasoning).

- Develop and fine tune LLMs for classification, summarization, RAG, and agentic workflows.

 

What We’re Looking For:

- PhD (preferred) or Master’s in CS, AI, ML, Data Science, or related fields.

Strong hands on experience with:

- Core ML & Stats (optimization, supervised/unsupervised learning)

- NLP (semantic search, embeddings, text modeling)

- MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD)

- Proficiency in Python, PyTorch/TensorFlow, HuggingFace, LangChain, and cloud platforms.

- Ability to translate complex ideas into scalable, real world systems.

- Knowledge Graphs (RDF/OWL, Neo4j, graph ML)

- LLMs & Transformers (fine tuning, RAG, prompt engineering)

 

Preferred

- Healthcare insurance / Managed Care (MCO) experience — familiarity with claims, clinical workflows, risk models, or regulatory frameworks is a strong plus.

- Experience with vector databases, hybrid semantic neural architectures, or agentic AI systems.

- Background in explainable or responsible AI.

 

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