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
