Lead Gen AI Engineer-Full time-Hybrid

We are looking for a Lead GenAI Engineer to design and deliver advanced AI-driven solutions, including RAG-based systems and intelligent AI agents. The ideal candidate will have strong experience in LLMs, agentic AI frameworks, and scalable AI architectures, with a passion for building production-grade GenAI applications.

Key Responsibilities

• Design and develop Generative AI solutions using LLMs, embeddings, and vector databases.

• Build and optimize RAG (Retrieval-Augmented Generation) pipelines including document processing, embeddings, and retrieval mechanisms.

• Develop AI agents capable of autonomous decision-making and multi-step reasoning.

• Work with multi-modal AI models and integrate them into enterprise applications.

• Implement data pipelines for preprocessing, transformation, and synthetic data generation.

• Ensure data quality, labeling, and structured data workflows for model training.

• Design and develop AI-powered automation workflows using Copilot Studio and Power Automate.

• Integrate AI solutions with enterprise systems, APIs, and external data sources.

• Collaborate with cross-functional teams to deliver scalable, secure, and high-performance AI applications.

• Monitor, evaluate, and optimize model performance, accuracy, and reliability in production.

Technical Skills

• Strong programming experience in Python.

• Hands-on experience with TensorFlow, PyTorch, and Hugging Face Transformers.

• Experience building RAG pipelines and working with vector databases.

• Knowledge of LLMs, prompt engineering, embeddings, and fine-tuning techniques.

• Experience designing AI agents using frameworks like LangChain, LangGraph, or similar.

• Familiarity with Copilot Studio, Power Automate, and workflow orchestration.

• Strong understanding of data pipelines, data preprocessing, and AI model lifecycle.

Preferred Skills

• Experience with multi-agent orchestration and autonomous AI systems.

• Knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform) for AI deployments.

• Familiarity with API integrations, microservices architecture, and DevOps practices.

• Experience in enterprise AI implementations and scalable system design.