Machine Learning Engineer (Neo4j)

Position: Machine Learning Engineer – Graph AI/Neo4j
Location: Hyderabad, India - Hybrid Remote (3 days in office, 2 days remote)
Experience: 2+ years
Employment Type: Full-time
Position Overview:
We are seeking a talented and experienced Machine Learning Engineer with a strong background in graph databases, particularly Neo4j, to join our dynamic team.

The ideal candidate will be instrumental in developing and enhancing our knowledge bases and Retrieval-Augmented Generation (RAG) models, driving the accuracy and efficiency of our AI-powered solutions.

You will play a key role in deploying cutting-edge models that enhance the AI features of our end-user applications, ensuring they meet the evolving needs of our customers.
Key Responsibilities
Develop and support machine learning models with a focus on graph-based data and Neo4j.
Build and maintain Python scripts and data pipelines for processing and analyzing graph data.
Work with Large Language Models (LLMs) and retrieval-augmented generation (RAG) techniques as part of the ML workflow.
Collaborate with backend and data teams to integrate graph AI solutions into applications.
Write clean, reusable code and participate in code reviews.
Support deployment and basic monitoring of ML models in production.
Document workflows and solutions for team knowledge sharing.
Must-Have Qualifications
2+ years of experience in Machine Learning or Data Science using Python.
Experience working with at least one graph database (preferably Neo4j) for data modeling and basic queries.
Good understanding of machine learning fundamentals (regression, classification, basic model evaluation).
Exposure to using or integrating LLMs (OpenAI, HuggingFace, or similar) with data workflows.
Basic knowledge of retrieval-augmented generation (RAG) concepts.
Familiarity with Python data libraries (pandas, scikit-learn, etc.).
Ability to work with RESTful APIs.
Familiarity with version control (Git) and writing simple unit tests.
Nice to Have
Hands-on experience building or optimizing graph ML models (e.g., node classification, link prediction).
Exposure to vector search or hybrid search techniques.
Experience deploying Python code or ML models using Docker or basic cloud services (AWS, GCP, Azure).
Experience working in a SaaS or multi-tenant application environment.
Key Skills
Python, Machine Learning, Graph Databases (Neo4j), LLM, RAG, Data Pipelines, Git, REST API