Role Overview :
We are seeking a highly motivated LLM Engineer with hands-on expertise in Large Language Models (LLMs) and Generative AI technologies. The ideal candidate will have experience in building end-to-end AI solutions, including RAG pipelines, vector databases, fine-tuning LLMs, and deploying scalable AI-powered applications. In this role, you will collaborate with cross-functional teams (data scientists, ML engineers, product managers) to design and deliver innovative AI systems that solve real-world business problems.
This is an exciting opportunity to work on state-of-the-art Generative AI models, influence technical strategy, and contribute to shaping the future of AI-driven solutions within the organization.
Key Responsibilities :
LLM Development & Deployment :
- Design, build, and deploy Generative AI applications leveraging models like GPT, LLaMA, Falcon, and other cutting-edge LLMs.
- Fine-tune and adapt pre-trained LLMs for domain-specific use cases.
RAG Pipelines & Vector Databases :
Develop Retrieval-Augmented Generation (RAG) pipelines for intelligent and contextual responses.Implement vector search solutions using ChromaDB, Pinecone, Weaviate, LanceDB, or FAISS.Prompt Engineering & Optimization :
Design effective prompt engineering strategies for reliable model outputs.Implement embeddings, semantic search, and context optimization to improve accuracy and relevance.AI Application Development :
Work with LangChain, Hugging Face Transformers, and related frameworks for application development.Ensure AI solutions are scalable, efficient, and & Best Practices :Work closely with data science, product, and engineering teams to deliver impactful solutions.Stay updated with the latest research in NLP, LLMs, and Generative AI and adopt best practices.Required Skills & Qualifications :
Programming : Strong proficiency in Python with experience in ML / AI libraries.LLM Experience : Minimum 2+ years hands-on with LLMs & Generative AI.Frameworks & Tools : Practical knowledge of LangChain, Hugging Face Transformers, BERT, GPT models.Vector Databases : Expertise in ChromaDB, Pinecone, LanceDB, Weaviate, or FAISS.RAG Pipelines : Proven track record of building, optimizing, and deploying RAG solutions.Deep Learning & NLP : Strong understanding of Neural Networks, NLP techniques, and embeddings.Deployment : Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus.Preferred Skills (Good to Have) :
Experience with MLOps workflows and deployment of AI models in production.Knowledge of LLMOps frameworks for monitoring and managing LLM-powered applications.Hands-on experience with APIs & microservices architecture.Understanding of responsible AI practices (bias, fairness, interpretability).(ref : hirist.tech)