GenAI Engineer

We are looking for an experienced GenAI Engineer to design and build next-generation AI applications using Google Gemini, Vertex AI, and the Google Cloud Platform (Google Cloud Platform) ecosystem. The ideal candidate will have strong expertise in LangChain, LangGraph, Retrieval-Augmented Generation (RAG), agentic AI workflows, and scalable cloud-native architectures. This role involves building production-grade AI solutions, integrating LLMs into enterprise applications, and developing intelligent multi-agent systems., * Design, develop, and deploy Generative AI applications powered by Google Gemini (Pro, Flash, Ultra) and Vertex AI.

• Build advanced prompt pipelines, RAG applications, and AI workflows using LangChain.
• Design and implement stateful, multi-agent AI systems using LangGraph.
• Develop scalable AI solutions utilizing Google Cloud services including Vertex AI Search, BigQuery, Cloud Run, Cloud Storage, and IAM.
• Build robust data ingestion pipelines supporting multiple document formats.
• Implement vector search architectures using Vertex AI Vector Search or vector databases such as Chroma, Milvus, Pinecone, Weaviate, or Qdrant.
• Optimize LLM performance using prompt engineering, few-shot learning, and PEFT techniques.
• Establish evaluation metrics for LLM accuracy, latency, hallucination detection, and model performance.
• Implement LLMOps best practices including observability, scalability, monitoring, and security.
• Develop REST APIs using FastAPI or Flask to expose AI services.
• Collaborate with Product Managers, Data Engineers, and Front-End Developers to integrate AI capabilities into enterprise applications.

• Strong programming experience in Python.
• Experience building REST APIs using FastAPI or Flask.
• Hands-on experience with Google Gemini APIs, Vertex AI, and other enterprise LLM platforms.
• Strong expertise with Langchain and LangGraph.
• Experience implementing RAG architecture.
• Strong knowledge of Google Cloud Platform (Google Cloud Platform).
• Experience with Vertex AI, IAM, Cloud Run, BigQuery, and Google Cloud Storage.
• Experience working with Vector Databases including Pinecone, Weaviate, Qdrant, Chroma, or Milvus.
• Strong SQL and NoSQL database experience.
• Experience debugging complex AI pipelines and distributed applications.
• Strong problem-solving and communication skills.

Preferred Qualifications

• Google Cloud Professional Machine Learning Engineer Certification.
• Google Cloud Professional Cloud Architect Certification.
• Experience with Llama Index.
• Experience with Hugging Face.
• Experience with React and TypeScript.
• Knowledge of Agentic AI architectures.
• Experience with MLOps or LLMOps platforms., * Python
• Google Gemini
• Vertex AI
• Google Cloud Platform (Google Cloud Platform)
• Langchain
• LangGraph
• FastAPI
• Flask
• RAG
• Prompt Engineering
• Vector Databases
• Pinecone
• Weaviate
• Qdrant
• Chroma
• Milvus
• BigQuery
• Cloud Run
• Google Cloud Storage
• REST APIs

Preferred Skills

• Llama Index
• Hugging Face
• React
• TypeScript
• PEFT
• LLMOps
• Agentic AI