AI/ML Engineer — AI Research & LLM ImplementationWe are looking for an AI/ML Engineer who can work across both research-oriented AI development and practical AI product implementation.This role is ideal for someone who understands modern AI systems deeply, can experiment with models and techniques, and can also build real working applications using LLMs, RAG pipelines, LangChain, OpenRouter, vector databases, and backend APIs.You will help us design, test, and deploy AI-powered systems that solve real problems, automate workflows, and create intelligent user experiences.ResponsibilitiesResearch, test, and evaluate modern AI/ML techniques, LLM architectures, prompting strategies, and retrieval methods.Build AI-powered applications using RAG, LangChain, OpenRouter, embeddings, vector search, and LLM APIs.Design and implement document-based question-answering systems, chatbots, AI agents, automation tools, and knowledge retrieval systems.Work with different LLM providers and APIs, including OpenAI, OpenRouter, Anthropic, Google, Hugging Face, and open-source models.Build and optimize RAG pipelines, including document parsing, chunking, embedding generation, vector storage, retrieval, reranking, and response generation.Experiment with prompt engineering, structured outputs, function calling, tool use, agent workflows, and multi-step reasoning systems.Develop backend services and APIs using Python, FastAPI, Django, Flask, or similar frameworks.Evaluate model outputs for accuracy, hallucination reduction, latency, cost, and reliability.Work with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Qdrant, pgvector, or similar tools.Improve AI system performance through better retrieval logic, prompt design, model selection, caching, and evaluation workflows.Stay updated with new AI models, frameworks, research papers, and practical AI engineering trends.RequirementsStrong Python programming skills.Hands-on experience building AI/ML or LLM-based applications.Practical experience with RAG, embeddings, vector databases, and semantic search.Experience using LangChain, OpenRouter, OpenAI APIs, Hugging Face, or similar AI tools.Good understanding of LLM behavior, prompt engineering, context windows, token usage, hallucinations, and model evaluation.Experience building backend APIs for AI-powered products.Ability to read AI/ML research papers and turn ideas into experiments or working prototypes.Strong problem-solving skills and ability to work independently.Clear communication and ability to explain technical ideas in simple language.Nice to HaveExperience with AI agents, tool calling, function calling, and workflow automation.Experience with fine-tuning, LoRA, quantization, or local model deployment.Knowledge of PyTorch, TensorFlow, scikit-learn, or traditional machine learning methods.Experience with MLOps, model serving, monitoring, and evaluation pipelines.Experience with cloud platforms such as AWS, Azure, or GCP.Experience with Docker, CI/CD, PostgreSQL, Redis, and scalable backend systems.Background in NLP, computer vision, recommendation systems, or generative AI research.Ideal CandidateThe ideal candidate is not only someone who can call an LLM API, but someone who understands how to build reliable AI systems around it.You should be able to research new AI techniques, compare different models, build RAG pipelines, test prompts, evaluate outputs, improve accuracy, and deploy useful AI features into production.We are looking for someone curious, technical, fast-learning, and comfortable working with both experimental AI research and practical software implementation.Application RequirementA Loom video recording is an essential part of the application process.In your Loom video, please introduce yourself, explain your AI/ML experience, describe one AI project you have built or researched, and tell us how you would approach building a RAG-based AI application.Applications without a Loom video will not be considered.