Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Specialized LLM/RAG skillset reduces broad applicant pool despite a known global services employer.
Core LLM and agentic AI skills transfer across industries but require ML/AI domain experience.
Mandatory hands-on LLM/RAG, Python and AWS skills plus prompt-engineering and deployment requirements raise strictness.
Design, develop, and deploy agentic AI solutions using Python, LLM, RAG architectures, and AWS to solve business problems.
Build and optimize intelligent workflows and Retrieval-Augmented Generation implementations to enhance response accuracy and contextual relevance.
Collaborate with product and operations stakeholders through co-design and iteration, maintaining artifacts in Git repositories and enabling stakeholder autonomy.
Proficiency in Python, Large Language Models (LLM), Retrieval-Augmented Generation (RAG), and AWS cloud services.
Strong experience in modern LLM application development including agent-based architectures and retrieval-focused AI solutions.
Hands-on experience with prompt engineering, evaluation frameworks, iterative model improvement, and API integration.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced engineer comfortable working closely with business stakeholders to co-design measurable and scalable AI solutions.
Proven ability to manage and optimize AI workflows including prompt evaluation and accuracy measurement in real-world applications.
Skilled in cloud-based AI solution deployment with strong expertise in AWS and Git-based CI/CD development workflows.