Mohammed Ishaq

Talent

MumbaiMember since 23 June 2026

PythonPrompt EngineeringPrompt OptimizationContext EngineeringLLM EvaluationRLHFVoice AIContext ManagementWorkflow AutomationRetrieval-Augmented Generation (RAG)Hallucination DetectionWorkflow ValidationOpenAI APIsVapiLangFlown8n

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PythonPrompt EngineeringPrompt OptimizationContext EngineeringLLM Evaluation

About

AI Voice and Conversational AI Engineer with 1.8 years of experience designing, evaluating, and optimizing enterprise Generative AI applications, Agentic AI workflows, and Retrieval-Augmented Generation (RAG) systems. Currently supporting AI initiatives at NVIDIA involving prompt engineering, RLHF workflows, dense captioning, question-answer generation, and workflow automation. Hands-on experience building Voice AI agents, RAG-powered assistants, and multi-agent workflows using Vapi, LangFlow, and n8n.

Skilled in prompt engineering, context engineering, prompt evaluation, hallucination detection, conversational design, workflow validation, and AI quality optimization. Adept at translating business requirements into scalable AI solutions while collaborating across research, engineering, and product teams.

Experience

Prompt Engineer - NVIDIA Graphics Pvt. Ltd. (Payroll: Randstad) - Pune, India

(2024-10)

Designed and optimized prompts for enterprise AI initiatives involving RLHF, dense video captioning, image captioning, and spatial question-answer generation workflows.
Developed reusable prompt libraries and structured prompt templates for internal query assistants, enabling consistent and scalable responses to guideline-driven employee queries.
Built an automated meeting summarization workflow that converted Microsoft Teams transcripts into structured Minutes of Meeting (MoM), reducing manual documentation effort.
Evaluated AI-generated responses for contextual accuracy, instruction adherence, response quality, and consistency across diverse LLM-driven use cases.
Conducted prompt testing, edge-case analysis, and iterative refinement to improve model behavior and reduce inconsistencies in generated outputs.
Collaborated with AI researchers, engineers, and data teams to improve prompt effectiveness, workflow reliability, and production readiness of enterprise AI solutions.
Supported workflow automation initiatives by simplifying prompt-driven processes and enabling scalable AI-assisted operations across internal teams.
Created evaluation scenarios, QA assets, and structured validation approaches to support continuous improvement of conversational and generative AI applications.