About This Role
This engineering role focuses on designing, developing, and deploying AI-powered solutions using state-of-the-art large language models (LLMs), multimodal models, and machine learning frameworks. It suits a Generative AI Engineer with hands-on experience in building GenAI applications, prompt engineering, fine-tuning models, and integrating AI solutions into production systems.
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
Requirements
- 2-11 years of experience
- 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
Nice to have:
- 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
Important Details
- Languages: Python, SQL
- Frameworks: PyTorch, TensorFlow
- GenAI Tools: LangChain, LlamaIndex
- Databases: Pinecone, FAISS, ChromaDB
- Cloud: AWS / Azure / GCP
- DevOps: Docker, Kubernetes
