• Position Overview:
  We are seeking a skilled Retrieval-Augmented Generation (RAG) Software Engineer to join our team. As a RAG Engineer, you will design, develop, and optimize AI systems that combine retrieval-based search mechanisms with generative AI models to create advanced, context-aware solutions. This role involves working with state-of-the-art technologies in machine learning, natural language processing (NLP), and information retrieval.

 

Key Responsibilities:

• System Design & Development:

• Architect and implement RAG-based systems that integrate retrieval mechanisms with generative AI models like GPT, LLaMA, or similar.

• Develop pipelines for indexing, storing, and retrieving large-scale datasets, ensuring low-latency access.

• Model Integration:

• Fine-tune and integrate large language models (LLMs) with retrieval systems to enhance contextual accuracy and relevancy.

• Collaborate on pre-training and fine-tuning tasks to optimize model performance for domain-specific applications.

• Data Engineering:

• Build scalable and efficient data pipelines for ingestion, preprocessing, and indexing of structured and unstructured data.

• Implement vector similarity search using tools like FAISS, Weaviate, or Pinecone.

• Performance Optimization:

• Optimize search and generation latency to meet real-time system requirements.

• Monitor and enhance the accuracy, relevance, and scalability of RAG systems.

• Collaboration:

• Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to align RAG solutions with business goals.

• Participate in research discussions and contribute to publications or technical blogs.

• Maintenance & Deployment:

• Deploy RAG systems into production environments and maintain their operational health.

• Ensure robust monitoring, logging, and error-handling mechanisms.

Required Qualifications:

• Educational Background:

• Bachelor's or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related fields.

• Technical Skills:

• Proficiency in programming languages such as Python, Java, or C++.

• Strong understanding of NLP and information retrieval concepts.

• Experience with frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.

• Hands-on experience with retrieval tools (e.g., ElasticSearch, FAISS, Weaviate, Pinecone).

• Familiarity with vector databases and similarity search techniques.

• Knowledge Areas:

• Solid grasp of transformer architectures and attention mechanisms.

• Familiarity with prompt engineering and large language model applications.

• Understanding of scalable backend systems, APIs, and microservices.

• Tooling:

• Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker and Kubernetes.

• Version control systems such as Git.

• Soft Skills:

• Strong analytical and problem-solving skills.

• Excellent communication and ability to explain technical concepts to non-technical stakeholders.

• Collaborative mindset and eagerness to work in a team environment.

Preferred Qualifications:

• Hands-on experience implementing retrieval-augmented generation in production systems.

• Familiarity with hybrid search techniques combining semantic and keyword-based retrieval.

• Exposure to emerging technologies like LangChain or OpenAI’s APIs.

• Publications or contributions to open-source RAG-related projects.