Key Responsibilities:
- Develop, fine-tune, and deployLarge Language Models (LLMs)for various applications, including chatbots, virtual assistants, and enterprise AI solutions.
- Build and optimizeconversational AI solutionswithat least 1 yearof experience in chatbot development.
- Implement and experiment withLLM agent development frameworkssuch asLangChain, LlamaIndex, AutoGen, and LangGraph.
- Design and developML/DL-based modelsto enhance natural language understanding capabilities.
- Work onretrieval-augmented generation (RAG)andvector databases(e.g., FAISS, Pinecone, Weaviate, ChromaDB) to enhance LLM-based applications.
- Optimize and fine-tune transformer-based models such asGPT, LLaMA, Falcon, Mistral, Claude, etc.for domain-specific tasks.
- Develop and implementprompt engineering techniquesandfine-tuning strategiesto improve LLM performance.
- Work onAI agents, multi-agent systems, and tool-use optimizationfor real-world business applications.
- Develop APIs and pipelines to integrate LLMs into enterprise applications.
- Research and stay up to date with the latest advancements inLLM architectures, frameworks, and AI trends.
Required Skills Qualifications:
- 5-8 years of experiencein Machine Learning (ML), Deep Learning (DL), and NLP-based model development.
- Hands-on experiencein developing and deploying conversational AI/chatbots is Plus
- Strong proficiency inPythonand experience with ML/DL frameworks such asTensorFlow, PyTorch, Hugging Face Transformers.
- Experience withLLM agent development frameworkslikeLangChain, LlamaIndex, AutoGen, LangGraph.
- Knowledge ofvector databases(e.g., FAISS, Pinecone, Weaviate, ChromaDB) andembedding models.
- Understanding ofPrompt EngineeringandFine-tuning LLMs.
- Familiarity withcloud services (AWS, GCP, Azure)for deploying LLMs at scale.
- Experience in working withAPIs, Docker, FastAPIfor model deployment.
- Strong analytical and problem-solving skills.
- Ability to work independently and collaboratively in a fast-paced environment.
Good to Have:
- Experience withMulti-modal AI models (text-to-image, text-to-video, speech synthesis, etc.).
- Knowledge ofKnowledge GraphsandSymbolic AI.
- Understanding ofMLOpsandLLMOpsfor deploying scalable AI solutions.
- Experience inautomated evaluation of LLMsandbias mitigation techniques.
