What We Are Looking For :

- 8 to 13 years of experience in ML/AI systems

- 2+ years of hands-on experience with LLMs (RAG, agents, prompt engineering)

- Strong proficiency in Python, LangGraph, and SQL

- Experience deploying GenAI systems on AWS / Azure / GCP

Good to Have - Knowledge Graph Expertise :

- Design and scale enterprise Knowledge Graph architectures

- Develop ontologies, taxonomies, and semantic data models

- Implement entity resolution, relationship extraction, and graph enrichment

- Experience with Neo4j, Amazon Neptune, or similar graph databases

- Strong hands-on experience with Cypher (or similar graph query languages)

- Build hybrid retrieval systems combining Knowledge Graphs + vector databases

- Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability

Roles & Responsibilities :

- Develop and optimize LLM-based solutions : Lead the design and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.

- Codebase ownership : Build and maintain/review high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.

- Cloud integration : Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.

- Cross-functional collaboration : Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.

- Mentoring and guidance : Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.

Requirements added by the job poster :

- 5+ years of work experience with Python (Programming Language)

- 3+ years of work experience with Retrieval-Augmented Generation (RAG)

- 2+ years of work experience with Knowledge Graph Augmentation