Conantville, CT · On-site
... Retrieval-Augmented Generation (RAG), and prompt engineering. * You will collaborate with data engineering and application development teams to build intelligent, scalable, and secure AI solutions ...
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Conantville, CT · On-site
... Retrieval-Augmented Generation (RAG), and prompt engineering. * You will collaborate with data engineering and application development teams to build intelligent, scalable, and secure AI solutions ...
... RAG (Retrieval-Augmented Generation) architecture and implementation - Prompt Engineering and LLM optimization - AI model monitoring, evaluation, and performance tuning - Vector databases and ...
... RAG (Retrieval-Augmented Generation) architecture and implementation - Prompt Engineering and LLM optimization - AI model monitoring, evaluation, and performance tuning - Vector databases and ...
A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.
A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.
To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.
Contractor
Re-posted 15 days ago
Hello There,
My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of AI Developer with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .
Job Description
Position : AI Developer
Location : Greenwich, CT Hybrid (Need local candidate)
Duration : 6+ Months
Interview : ONSITE INTERVIEW IS A MUST
Description
Key Responsibilities
Qualifications
