Job Description

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Job Type

Remote(US time zone)

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Experience

10 +

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Location

Remote (US Timing)

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Looking for a skilled and driven GenAI Engineer to join our team. You will be instrumental in designing and deploying a cutting-edge semantic search capability to power our generation of enterprise AI applications. This role is perfect for an engineer who thrives on building production-ready LLM-powered retrieval systems from the ground up.




As a GenAI Engineer, your primary focus will be on the end-to-end development of Retrieval- Augmented Generation (RAG) systems. You will leverage Amazon Bedrock, OpenSearch, and advanced vector embedding pipelines to deliver highly relevant and performant search and retrieval services.

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Key Responsibilities

Design and implement robust semantic search architectures utilizing Amazon Bedrock and OpenSearch (specifically k-NN indexing and vector embeddings). Build automated embedding pipelines for data ingestion and indexing using core AWS services like Lambda, S3, and API Gateway. Integrate multimodal embeddings (e.g., Titan Multimodal or similar models) to enable high-relevance search across both text and image data. Develop scalable REST APIs for handling semantic queries, sophisticated result ranking, and hybrid search (combining semantic relevance with filters). Collaborate closely with backend and data engineering teams to seamlessly integrate developed AI services into existing enterprise applications. Lead efforts in search relevance testing, optimization, and comprehensive technical documentation. #

Qualifications

Familiarity with PostgreSQL or experience using workflow automation tools like n8n. Experience successfully integrating and deploying AI features into production web or large-scale enterprise systems.

Required Skills & Experience



Expert-level proficiency in Python and developing robust, scalable REST APIs. Proven experience working with Amazon Bedrock, OpenSearch, and various vector databases ( Weaviate, Pinecone, etc.). Deep technical understanding of LLM architectures, embeddings, and designing RAG pipelines. Practical experience with key AWS services including Lambda, API Gateway, S3, and Step Functions. Demonstrated ability to evaluate embedding quality, perform iterative tuning, and optimize search relevance. * Strong problem-solving, communication, and collaboration skills, particularly in agile and fast-paced environments.