GEN AI

Job description

Job Title: Generative AI Engineer / AI Engineer Experience: 2 11 Years Employment Type: Full-Time Job Summary We are seeking a passionate Generative AI Engineer to design, develop, and deploy AI-powered solutions using state-of-the-art large language models (LLMs), multimodal models, and machine learning frameworks. The ideal candidate will have hands-on experience in building GenAI applications, prompt engineering, fine-tuning models, and integrating AI solutions into production systems. Key Responsibilities • Design and develop Generative AI applications using LLMs (GPT, Llama, Claude, etc.) • Build end-to-end AI pipelines including data ingestion, preprocessing, model training, and deployment • Implement prompt engineering and prompt optimization techniques • Develop and maintain RAG (Retrieval-Augmented Generation) systems • Fine-tune and customize open-source and proprietary large language models • Integrate AI services via APIs and SDKs into enterprise applications • Work with vector databases (FAISS, Pinecone, Weaviate, ChromaDB) • Ensure model performance, scalability, and reliability in production • Collaborate with cross-functional teams (Data Scientists, DevOps, Backend Engineers) • Monitor and evaluate LLM outputs for accuracy, safety, and bias • Stay updated with latest advancements in AI/ML and GenAI ecosystems Required Skills • Strong programming skills in Python • Experience with Generative AI frameworks: • LangChain, LlamaIndex, Semantic Kernel • Hands-on experience with LLMs: • OpenAI GPT, Hugging Face, Llama, Mistral, etc. • Knowledge of prompt engineering techniques • Experience with vector databases • Familiarity with RAG architectures • Understanding of ML/DL fundamentals • Experience with REST APIs and microservices Preferred Skills • Experience in fine-tuning LLMs (LoRA, PEFT, RLHF) • Knowledge of multi-modal models (text, image, audio) • Exposure to MLOps tools (MLflow, Kubeflow, Airflow) • Experience with cloud platforms (AWS, Azure, GCP) • Familiarity with Docker, Kubernetes • Understanding of data pipelines and big data tools • Knowledge of LLM evaluation frameworks Tools & Technologies • Languages: Python, SQL • Frameworks: PyTorch, TensorFlow • GenAI Tools: LangChain, LlamaIndex • Databases: Pinecone, FAISS, ChromaDB • Cloud: AWS / Azure / GCP • DevOps: Docker, Kubernetes