GenAI Engineer

As a Mid-Level AI Engineer at our company, you will be an integral part of our AI team, focusing on the development of intelligent applications using Python, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) systems. Your collaboration with data scientists, backend engineers, and product teams will be pivotal in building and deploying AI-powered solutions that bring real-world value. Your responsibilities will include designing, developing, and optimizing applications by leveraging LLMs such as GPT, LLaMA, and Claude. You will also be responsible for implementing RAG pipelines to enhance LLM performance through domain-specific knowledge bases and search tools. Developing and maintaining robust Python codebases for AI-driven solutions, integrating vector databases like Pinecone, Weaviate, FAISS, and embedding models for information retrieval will be a key part of your role. Additionally, you will work with APIs, frameworks like LangChain, Haystack, and tools to build scalable AI workflows. Collaborating with product and design teams to define AI use cases and deliver impactful features, conducting experiments to evaluate model performance, retrieval relevance, and system latency, as well as staying updated on research and advancements in LLMs, RAG, and AI infrastructure are also important aspects of your responsibilities. To be successful in this role, you should have at least 3-5 years of experience in software engineering or AI/ML engineering, with a strong proficiency in Python. Experience working with LLMs from OpenAI, Hugging Face Transformers, etc., hands-on experience with RAG architecture, vector-based retrieval techniques, embedding models like SentenceTransformers, OpenAI embeddings, and vector databases such as Pinecone, FAISS is required. Knowledge of API design, deployment, performance optimization, version control using Git, containerization with Docker, and cloud platforms like AWS, GCP, Azure is essential. Preferred qualifications include experience with LangChain, Haystack, or similar LLM orchestration frameworks, understanding of NLP evaluation metrics, prompt engineering best practices, exposure to knowledge graphs, semantic search, document parsing pipelines, experience in deploying models in production, monitoring system performance, and contributions to open-source AI/ML projects are a plus.,