Gen AI Engineer
1 week ago
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.
Roles and Responsibilities
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).
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