LLM/Prompt Engineer

Job Summary :

We are looking for a highly motivated LLM Engineer / Prompt Engineer with hands-on experience in Python, Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG). The ideal candidate will be responsible for designing, developing, and deploying AI-powered solutions using modern LLM frameworks while ensuring high-quality, scalable, and production-ready implementations. The candidate should have strong expertise in NLP, data processing, prompt optimization, vector databases, and AI application development, along with the ability to collaborate with cross-functional teams to deliver innovative AI-driven solutions.

Education :

- Bachelor's degree in Engineering, Computer Science, Information Technology, or a related discipline from a recognized university.

- Master's degree in a relevant field is preferred.

Experience :

- 4 years of hands-on experience in Python development and AI/ML applications.

- Experience in developing and deploying AI-powered applications using Large Language Models (LLMs).

- Strong understanding of software engineering best practices and scalable application development.

Key Skills & Qualifications :

1. Technical Skills :

- Strong proficiency in Python with experience writing reusable, efficient, testable, and scalable code.

- Hands-on experience in Prompt Engineering for optimizing LLM responses across various use cases.

- Practical experience with Large Language Models (LLMs) such as OpenAI, Llama, Mistral, Gemini, or similar.

- Strong understanding of Retrieval-Augmented Generation (RAG) architecture and implementation.

- Experience working with Vector Databases such as Pinecone, ChromaDB, FAISS, Weaviate, or Milvus.

- Experience using LangChain, LangGraph, or similar LLM orchestration frameworks.

- Familiarity with embedding models (e.g., Hugging Face Sentence Transformers) and vector representation techniques.

- Experience integrating LLMs into production applications such as AI assistants, chatbots, copilots, and intelligent agents.

- Strong understanding of Agentic AI concepts and multi-agent workflows.

- Experience with Natural Language Processing (NLP) tasks including :

1. Text summarization

2. Sentiment analysis

3. Text classification

4. Information extraction

- Experience cleaning, preprocessing, and analyzing structured and unstructured text data.

- Hands-on experience with web scraping using Selenium or similar automation frameworks.

- Experience extracting and processing data from multiple sources including web pages, documents, images, and APIs.

- Experience building reusable and automated data processing pipelines.

- Strong understanding of Machine Learning fundamentals.

- Experience working with SQL and NoSQL databases.

- Knowledge of REST APIs and AI model integration.

- Familiarity with web frameworks such as Flask or Streamlit is an added advantage.

Roles & Responsibilities :

- Design, develop, and deploy AI-powered applications using Large Language Models (LLMs).

- Develop effective prompts and prompt engineering strategies to improve model accuracy and performance.

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.

- Design intelligent AI assistants, chatbots, copilots, and agent-based applications.

- Develop scalable Python applications following coding standards and best practices.

- Build data ingestion and processing pipelines for structured and unstructured datasets.

- Extract data from multiple sources including websites, documents, images, APIs, and enterprise systems.

- Clean, preprocess, and transform data to support AI and machine learning workflows.

- Evaluate, monitor, and improve LLM output quality, accuracy, and reliability.

- Identify prompt failures, hallucinations, and model limitations, and implement mitigation strategies.

- Collaborate with business stakeholders, engineering teams, and product teams to understand business requirements and translate them into AI solutions.

- Assess technical feasibility and recommend appropriate AI architectures and implementation approaches.

- Participate in solution design, system integration, and application enhancement activities.

- Ensure adherence to coding standards, security practices, and software development best practices.

- Maintain documentation for AI workflows, prompt libraries, and technical implementations.

- Provide regular project updates and communicate progress, risks, and dependencies to the Project Manager.

Preferred Skills :

- Experience with cloud platforms such as AWS, Azure, or Google Cloud.

- Knowledge of Docker, Kubernetes, and CI/CD pipelines.

- Experience with MLOps or LLMOps tools.

- Familiarity with model evaluation frameworks and prompt testing methodologies.

- Understanding of AI governance, responsible AI practices, and model monitoring.

Key Competencies :

- Strong analytical and problem-solving skills.

- Excellent communication and stakeholder management abilities.

- Ability to work independently as well as in a collaborative team environment.

- Strong attention to detail and commitment to delivering high-quality AI solutions.

- Passion for Generative AI and emerging technologies.