Full Stack Engineer - Large Language Models (LLM) & AI/ML

LLM Full Stack Engineer

THE WORK: Explore the fascinating world of Large Language Models and immerse yourself in a role where your independent contributions will shape innovative solutions. You will actively participate in discussions and collaborate to provide thoughtful answers to complex challenges. This opportunity invites you to grow as a subject matter expert while engaging with cutting-edge technology. Join us in this exciting journey and make a meaningful impact. Responsibilities: Design and develop AI/ML and Generative AI solutions using Python Build and deploy applications leveraging Large Language Models (LLMs) Develop end-to-end pipelines including data ingestion, preprocessing, model integration, and deployment Implement prompt engineering and fine-tuning strategies for LLMs Integrate AI services into web/mobile/backend applications via APIs Work with vector databases and embeddings for semantic search and RAG architectures Optimize models and pipelines for performance, latency, and cost Collaborate with cross-functional teams to translate business requirements into AI solutions Ensure responsible AI practices including data privacy, bias mitigation, and explainability Monitor and improve model performance in production environments ________________________________________ Required Skills & Qualifications 6-8 years of experience in software engineering / AI / ML, Python roles, Strong programming expertise in Python Hands-on experience with ML/DL frameworks like PyTorch or TensorFlow Experience with LLM frameworks such as LangChain or LlamaIndex Solid understanding of NLP concepts and transformer-based models Experience with REST APIs and microservices architecture Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) Experience with version control systems like Git ________________________________________ Preferred Qualifications Experience working with APIs from providers like OpenAI or open-source LLMs (Llama, Mistral) Knowledge of Retrieval-Augmented Generation (RAG) architectures Experience with model fine-tuning and evaluation techniques Exposure to cloud platforms (AWS / GCP) for deployment (excluding Azure) Understanding of containerization tools like Docker ________________________________________ Soft Skills Strong analytical and problem-solving skills Ability to translate complex AI concepts into practical solutions Good communication and stakeholder management skills Ability to work independently and in collaborative environments ________________________________________ Nice to Have Experience with AI agents / autonomous workflows Knowledge of MLOps practices and CI/CD for ML pipelines Exposure to multimodal AI (text, image, audio models) Experience with real-time AI applications (chatbots, voice bots, copilots)