AI Engineer

Introduction

Your Role And Responsibilities

• AI Development and Implementation:
• Design, develop, and deploy cutting-edge AI solutions using GenAI technologies.
• Implement and optimize Large Language Models (LLMs) for texts, image and other data.
• Leverage Retrieval-Augmented Generation (RAG) techniques to enhance AI-driven insights and decision-making.
• Enterprise Software Integration:
• Integrate AI models with Enterprise Software to build high-quality AI use-cases.
• Develop custom AI algorithms and AI Agents.
• Solution Optimization and Integration:
• Fine-tune NLP models to align with domain-specific needs and improve accuracy.
• Collaborate with cross-functional teams to integrate AI solutions into enterprise workflows.
• Mentorship and Leadership:
• Mentor junior developers on AI best practices, tools, and frameworks.
• Lead brainstorming sessions to explore innovative AI applications and solutions.
• Research and Innovation:
• Stay updated with the latest advancements in AI, GenAI, LLMs, and RAG to continuously enhance systems.
• Conduct proof-of-concept projects to evaluate emerging technologies for business use cases.
  

Preferred Education

Required Technical And Professional Expertise

• 5+ years of experience.
  

Programming Expertise

• Proficient in Python with demonstrated experience in AI and ML development. Knowledge of vector database.

• AI Agents, GenAI and LLMs:
• Strong expertise in GenAI concepts, including the design, deployment, and optimization of Large Language Models (LLMs) and design AI Agents.
• NLP Skills:
• Advanced knowledge of NLP techniques for text analysis, summarization, and contextual understanding.
• RAG Expertise:
• Hands-on experience with Retrieval-Augmented Generation (RAG) to enhance knowledge retrieval systems.
• Tools and Frameworks:
• Proficiency with AI frameworks like TensorFlow, PyTorch, Hugging Face, or similar.
• Familiarity with cloud platforms (AWS, Azure, GCP) for deploying AI solutions.
  

Preferred Technical And Professional Experience

• DevOps and MLOps:
• Knowledge of deploying AI models in production environments using MLOps best practices.
• Data Engineering:
• Familiarity with data preparation, preprocessing pipelines, and working with unstructured datasets.
• Agile Methodologies:
• Experience working in Agile development environments for iterative delivery.
• Soft Skills:
• Strong problem-solving skills, attention to detail, and ability to communicate complex concepts to technical and non-technical stakeholders.