Gen AI Engineer

We are seeking a highly skilled Generative AI Engineer with 4–7 years of professional experience to design, develop, and optimize GenAI solutions. The ideal candidate will have strong expertise in Retrieval-Augmented Generation (RAG) architecture, unstructured data processing, vector database management, and cloud-based data engineering using GCP technologies. 

Required Skills

- Strong understanding of RAG (Retrieval-Augmented Generation) architecture. 

- Hands-on experience with data ingestion and processing of unstructured files (PPTX, DOCX, PDF). 

- Practical experience in vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus). 

- Proficiency in Python for AI pipeline development and SQL for complex queries. 

- Exposure to GCP services (BigQuery, Dataflow, GCS, Pub/Sub). 

- Familiarity with DevOps processes such as CI/CD, containerization (Docker), and orchestration (Kubernetes). 

- Strong problem-solving and debugging skills. 

 Nice-to-Have

- Knowledge of Graph Databases (e.g., Neo4j, TigerGraph). 

- Experience contributing to end-to-end GenAI product deployment in production. 

- Exposure to ML model fine-tuning and optimization for domain-specific use cases. 

- Design and implement RAG-based solutions by integrating LLMs with structured and unstructured datasets. 

- Process and transform unstructured data from multiple file types (e.g., PPTX, DOCX, PDF) into usable formats for AI/ML workflows. 

- Develop efficient data chunking, embedding, and indexing pipelines into vector databases for retrieval tasks. 

- Build and optimize GenAI-based applications leveraging foundational AI/ML concepts. 

- Write efficient and clean Python and SQL code to process, transform, and query data. 

- Work with GCP services such as BigQuery, Dataflow, and Pub/Sub to build scalable data pipelines. 

- Contribute to DevOps processes including CI/CD pipelines, automation, and deployment of AI/ML models. 

- Collaborate with data scientists, engineers, and product teams to integrate AI into business applications. 

- Explore and drive POCs around Graph Databases for relationship-based data retrieval (good-to-have).