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
Solid 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

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