LLM Engineer

Job Title: LLM Engineer (Large Language Model Engineer) Job Summary: We are seeking a highly skilled LLM Engineer to design, develop, fine-tune, and deploy Large Language Model (LLM) based applications and AI-powered solutions. The ideal candidate will have expertise in Generative AI, Natural Language Processing (NLP), Prompt Engineering, Retrieval-Augmented Generation (RAG), and AI system architecture. This role involves building intelligent assistants, AI copilots, enterprise search platforms, and automation solutions using cutting-edge LLM technologies. Key Responsibilities: Design, develop, and deploy applications powered by Large Language Models (LLMs). Build AI assistants, chatbots, knowledge management systems, and AI copilots. Develop and optimize Prompt Engineering strategies to improve model responses. Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases. Fine-tune open-source and proprietary LLMs for domain-specific use cases. Integrate AI models with enterprise systems, APIs, databases, and cloud services. Develop evaluation frameworks to measure model performance, accuracy, and reliability. Implement guardrails, safety mechanisms, and hallucination mitigation techniques. Collaborate with Data Scientists, AI Engineers, Product Managers, and MLOps teams. Monitor, optimize, and maintain AI systems in production environments. Research and evaluate emerging AI models, frameworks, and architectures. Ensure compliance with Responsible AI, security, and governance standards. Required Skills: Strong understanding of Large Language Models (LLMs) and Generative AI concepts. Experience with NLP, Transformer architectures, and AI model deployment. Expertise in Prompt Engineering and Retrieval-Augmented Generation (RAG). Knowledge of AI evaluation, model optimization, and inference strategies. Strong problem-solving and system design skills. Excellent communication and collaboration abilities. Technical Skills: Programming Languages: Python, SQL, JavaScript LLM Platforms: OpenAI GPT, Claude, Gemini, Llama, Mistral, Cohere AI Frameworks: LangChain, LlamaIndex, LangGraph, Haystack Machine Learning Frameworks: PyTorch, TensorFlow, Hugging Face Transformers Vector Databases: Pinecone, ChromaDB, Weaviate, Milvus, FAISS Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP) APIs: REST APIs, GraphQL MLOps Tools: MLflow, Kubeflow, Airflow Containerization: Docker, Kubernetes Version Control: Git, GitHub, GitLab Qualifications: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. Master's degree in AI, Data Science, or Machine Learning is a plus. Relevant AI and cloud certifications are preferred. Experience: 3-8 years of experience in AI Engineering, Machine Learning Engineering, NLP, or Software Development. Hands-on experience building and deploying LLM-powered applications. Experience with RAG architectures, vector databases, and AI orchestration frameworks. Experience integrating AI services into enterprise applications. Preferred Qualifications: Experience with fine-tuning techniques such as LoRA, PEFT, RLHF, and QLoRA. Knowledge of Agentic AI, Multi-Agent Systems, and Autonomous AI Workflows. Experience with LangGraph, CrewAI, AutoGen, or Semantic Kernel. Understanding of AI Security, Responsible AI, and Model Governance. Experience deploying AI applications in cloud-native environments. Preferred Qualities: Strong innovation and research mindset. Passion for AI, NLP, and emerging technologies. Excellent troubleshooting and analytical skills. Ability to rapidly learn and adapt to new AI frameworks. Strong ownership and accountability for AI solution quality. Employment Type: Full-Time Location: Remote / Hybrid / On-site Nice to Have: Experience building AI copilots, enterprise search systems, and knowledge assistants. Knowledge of multimodal AI models (text, image, audio, video). Experience with AI observability and evaluation platforms such as LangSmith, Arize AI, or Weights & Biases. Familiarity with enterprise SaaS, HR Tech, Healthcare, FinTech, E-commerce, or Customer Support AI solutions. Contributions to open-source AI projects, research papers, AI communities, or hackathons.