Gen AI Engineer

1 week ago


Bengaluru Chennai Hyderabad, India InfoVision Inc Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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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