Generative AI Developer

Job Title: Generative AI Developer Job Description: We are seeking a skilled Generative AI Developer with hands-on experience in Large Language Models (LLMs) , prompt engineering , and modern GenAI solution design. The ideal candidate should be comfortable working with Retrieval-Augmented Generation (RAG) , Agents frameworks, and AWS Bedrock , with a basic understanding of LLMOps practices. Additionally, proficiency in Streamlit for developing quick front-end prototypes of GenAI applications is highly desirable. The role involves building scalable, real-world GenAI applications and continuously exploring advancements in the field of Generative AI. Key Responsibilities: Develop and maintain GenAI solutions using LLMs, RAG pipelines, and agent-based workflows. Integrate LLMs into applications using APIs and cloud-based services like AWS Bedrock . Conduct experiments and research on the latest GenAI advancements and implement proof-of-concepts (PoCs). Contribute to LLMOps practices including prompt versioning, testing, and basic monitoring. Collaborate with architects, data engineers, and DevOps teams to build scalable GenAI solutions. Technical Requirements: 6-8 years of total experience with at least 3+ years of hands-on experience in AI/ML or NLP. Hands-on experience working with LLMs (e.g., GPT, Claude, LLaMA, Mistral). Understanding of RAG workflows and experience with vector databases (e.g., FAISS, Pinecone, OpenSearch). Exposure to agent frameworks like Crewai, LangGraph, Semantic Kernel, or custom orchestrators. Familiarity with AWS Bedrock and prompt orchestration through tools like LangChain or PromptLayer. Knowledge of basic LLMOps practices such as prompt versioning, monitoring, and logging. Proven experience in prompt engineering designing few-shot examples, controlling hallucinations, and optimizing outputs. Proficient in Streamlit for building lightweight, interactive front-ends. Strong programming skills in Python and experience with REST APIs. Good in Deep Learning architectures: RNN, CNN, Transformer-based models Experience with NLP using open-source libraries (e.g., spaCy, Hugging Face, NLTK) Familiarity with data storage and search systems such as PostgreSQL , Elasticsearch , or similar. Preferred Skills: Exposure to document processing pipelines and unstructured data extraction. Knowledge of MLOps and CI/CD tools (e.g., GitHub Actions, Docker). Prior experience integrating GenAI into real-world applications Soft Skills: Strong analytical and problem-solving skills with the ability to break down complex challenges. Curiosity and initiative in researching new GenAI trends and applying them practically. Good communication and collaboration skills, especially in a cross-functional team environment.