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.