Generative AI Engineer

Description :

We are seeking a Generative AI Engineer with 3.5+ years of experience to work with cutting-edge AI technologies, including RAG (Retrieval-Augmented Generation), Agentic AI, and transformer-based architectures, along with strong Python programming skills, to design, implement, and optimize AI-driven data solutions.

Roles & Responsibilities :

- Design and implement AI-powered data pipelines leveraging RAG models and transformers for advanced knowledge retrieval and generation tasks.

- Utilize LangChain, LangGraph, and other frameworks to build and integrate intelligent workflows and conversational agents.

- Develop Agentic AI solutions to create autonomous, goal-driven agents that interact with dynamic environments and data.

- Optimize data storage, processing, and retrieval techniques for large-scale AI platforms.

- Engineer prompt structures for optimized performance in generative AI tasks, including natural language processing (NLP) and multimodal applications.

- Ensure robust data privacy and security measures are in place for all AI-driven systems.

- Continuously monitor, troubleshoot, and enhance AI system performance.

- Collaborate with cross-functional teams (e.g., Data Scientists, Engineers) to integrate generative AI models into real-world data solutions.

Required Technical Skills :

- 3.5+ years of hands-on experience in Python for building AI models and systems.

- Strong expertise in Generative AI frameworks, such as RAG (Retrieval-Augmented Generation), transformers, and agentic models.

- Proficiency in LangChain, LangGraph, and Agentic AI frameworks to develop intelligent, goal-oriented agents and workflows.

- Hands-on experience with prompt engineering for optimizing generative AI outputs.

- Strong experience with cloud-based platforms like AWS, Azure, or GCP for deploying AI solutions.

- Deep understanding of data governance, compliance, and security standards in AI applications.

- Familiarity with NLP libraries (like Hugging Face, spaCy, NLTK) and machine learning frameworks (like TensorFlow, PyTorch).

- Experience working with Databricks or similar platforms for AI/ML model deployment and scaling.