AI Engineer

Job Description

AI Development and Implementation: Design, develop, and deploy cutting-edge AI solutions using GenAI technologies. Implement and optimize Large Language Models (LLMs) for natural language processing (NLP) applications. Leverage Retrieval-Augmented Generation (RAG) techniques to enhance AI-driven insights and decision-making. Requirements Quality Management: Integrate AI models with Requirements Management Software tool to ensure high-quality requirements management. Develop custom AI algorithms to automate requirement validation, improve quality, and detect inconsistencies. 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. Required education Bachelor's Degree Required technical and professional expertise Programming Expertise: Proficient in Python with demonstrated experience in AI and ML development. GenAI and LLMs: Strong expertise in GenAI concepts, including the design, deployment, and optimization of Large Language Models (LLMs). 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.