GEN AI

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