Junior Large Language Models And Prompt Engineering Specialist

What role will you play in the team

As a Junior Large Language Models and Prompt Engineering Specialist based in Coimbatore, you will drive the experimentation, fine-tuning, and deployment of generative AI solutions within our Data Science, Analytics & Machine Learning unit. Your role centers on bridging the gap between raw foundational models and enterprise-grade natural language processing applications. You will work closely with domain experts to construct sophisticated prompt workflows, implement retrieval-augmented generation architectures, and ensure that our text-based AI agents deliver precise, context-aware, and safe responses for our global clientele.

What you will do

You will experiment daily with state-of-the-art foundational models using the OpenAI API, Claude API, and open-source LLMs deployed via Hugging Face. Your responsibilities include crafting, testing, and iterating on complex prompt templates, integrating APIs and webhooks into existing enterprise software, and building RAG pipelines to connect LLMs with proprietary corporate databases like MongoDB and PostgreSQL. You will also monitor model outputs for hallucinations, bias, and toxic content, implementing guardrails and evaluation frameworks to maintain strict quality standards.

Key responsibility

• Design, test, and optimize advanced prompt engineering templates and chains for diverse natural language processing use cases.
• Implement Retrieval-Augmented Generation (RAG) architectures to ground LLM responses in verified corporate document repositories.
• Integrate OpenAI API, Claude API, and other third-party generative AI endpoints with backend applications using RESTful APIs and webhooks.
• Fine-tune open-source large language models using parameter-efficient fine-tuning techniques on specialized text corpora.
• Collaborate with data engineers to ingest, clean, and structure unstructured textual data into MongoDB and PostgreSQL databases.
• Build automated workflow automation sequences using tools like n8n, Flowise, and Zapier to connect AI models with operational software.
• Evaluate model hallucination rates, semantic similarity, and task completion accuracy using rigorous quantitative metrics and testing suites.

Required Qualification and Skills

• Bachelor's degree in Computer Science, Information Technology, Computational Linguistics, or a related technical field.
• 0-1 years of experience or specialized project work involving Large Language Models, prompt engineering, and generative AI deployment.
• Strong programming skills in Python with familiarity in libraries such as scikit-learn, Pandas, and LangChain.
• Hands-on experience working with OpenAI API, Claude API, Hugging Face transformers, and vector databases.
• Familiarity with database management systems including MongoDB, PostgreSQL, and MySQL.
• Knowledge of workflow orchestration tools such as n8n, Flowise, Zapier, and make.com.
• Exceptional analytical thinking, meticulous attention to detail, and a passion for advancing conversational artificial intelligence technologies.

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Benefits Included

• Attractive starting salary package with regular performance reviews and rewarding milestone bonuses.
• Comprehensive medical, accidental, and life insurance policies for employees and their nominees.
• Dedicated annual learning stipend for purchasing technical books, attending AI conferences, and taking certified online courses.
• Flexible work arrangements including hybrid options and generous paid time off.
• Modern, collaborative office environment equipped with high-speed fiber internet and relaxation zones.
• Mentorship opportunities with seasoned AI researchers and enterprise architects.

A Day in the Life

Your day begins by analyzing performance logs from an enterprise customer support chatbot deployed the previous week. You identify a cluster of ambiguous queries and spend the morning crafting refined system prompts and few-shot examples using the Claude API. After lunch, you collaborate with the data engineering team to update the vector embedding database in MongoDB for a new RAG pipeline. The afternoon wraps up with testing webhook integrations via n8n and presenting your prompt optimization results to the product strategy team.