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