We are seeking an experienced Machine Learning Engineer specializing in Large Language Models (LLMs), Natural Language Processing (NLP), and AI Agents. In this role, you will design, develop, and deploy cutting-edge language models and agent-based systems that drive innovation and deliver value across products and services. Key Responsibilities * Design, fine-tune, and optimize large language models (LLMs) for specific use cases and domains * Develop and implement prompt engineering techniques to enhance model capabilities and performance * Create autonomous AI agents capable of performing complex tasks through language understanding and reasoning * Build retrieval-augmented generation (RAG) systems that combine LLMs with external knowledge sources * Implement evaluation frameworks to benchmark LLM and agent performance across various metrics * Optimize models for production deployment with attention to latency, throughput, and cost * Design conversational flows and dialogue management systems for AI assistants * Develop techniques to ensure responsible AI deployment, including safety guardrails and bias mitigation * Stay current with the rapidly evolving NLP/LLM research landscape and implement state-of-the-art techniques
Requirements * Strong experience with transformer-based models and frameworks (Hugging Face, PyTorch, TensorFlow) * Practical experience fine-tuning, serving, and optimizing LLMs (e.g., GPT models, Llama, Claude, Mistral) * Expertise in NLP techniques including prompt engineering, embeddings, and semantic search * Experience with vector databases and retrieval systems (Pinecone, Weaviate, Faiss, etc.) * Familiarity with model quantization, distillation, and other optimization techniques * Experience with LLM orchestration frameworks (LangChain, LlamaIndex, etc.) * Working knowledge of cloud platforms (AWS, GCP, Azure) for ML model deployment * Proficiency with containerization technologies (Docker, Kubernetes) * Strong understanding of LLM evaluation methodologies and metrics
Nice-to-Have skills: * Experience developing multi-agent systems or agent-based architectures * Knowledge of reinforcement learning from human feedback (RLHF) techniques * Familiarity with LLM inference optimization (vLLM, TensorRT, ONNX) * Experience with tools for LLM observability, evaluation, and debugging * Experience with multimodal models combining text, vision, and audio * Knowledge of LLM security, safety, and alignment techniques
