Prompt Engineer (Entry Level: 0 2 Years Experience)
Key Responsibilities
Prompt Engineering & Optimization
Design, refine, and evaluate prompts for LLMs to support summarization, extraction, reasoning tasks, classification, conversational flows, and automation workflows.
Apply systematic techniques such as few-shot prompting, structured chaining, and guardrail prompting to improve accuracy, reliability, and safety.
Develop reusable domain-specific prompt templates and contribute to internal AI accelerator libraries.
AI Service Integration (Python + Java)
Build Python microservices using FastAPI/Flask, LangChain, LlamaIndex, or custom RAG pipelines to deliver AI inference capabilities.
Work collaboratively with Java development teams to integrate AI endpoints, model inference logic, and semantic search features into enterprise Java applications.
Assist Java engineers in developing supporting components, writing sample integrations, implementing SDK wrappers, and troubleshooting performance or compatibility issues.
Data Preparation & Model Evaluation
Prepare, preprocess, and normalize datasets for prompt testing and model evaluation.
Build automated evaluation scripts in Python to assess accuracy, bias, toxicity, robustness, and completeness of LLM outputs.
Conduct multi-variant testing (A/B/C prompt comparisons) and provide documented insights to senior engineers.
Software Development & Automation
Contribute to end-to-end AI application development using both Python and Java as required by project needs.
Participate in CI/CD processes using GitHub, Jenkins, or Azure DevOps to automate build, test, and deployment workflows.
Develop supporting utilities for logging, monitoring, RAG indexing, and model lifecycle management.
Documentation & Technical Knowledge Transfer
Produce clear documentation for prompt libraries, API usage, system design, and troubleshooting.
Collaborate with Java programmers to ensure smooth adoption of AI features and provide technical guidance on how AI services will be consumed by Java-based systems.
Prepare internal playbooks for integrating LLMs into multi-tier architectures.
Required Skills & Qualifications
Education
Bachelor s degree in Computer Science, Software Engineering, Data Science, or related field.
Technical Skills (Mandatory)
Strong programming skills in Python for building AI pipelines, APIs, and evaluation frameworks.
Working proficiency in Java, with ability to help Java teams integrate AI components and build supporting logic.
Understanding of LLM capabilities, tokenization, embeddings, vector databases (FAISS, Pinecone, Chroma), and RAG concepts.
Familiarity with AI/ML frameworks like LangChain, HuggingFace Transformers, OpenAI API, and LlamaIndex.
Knowledge of RESTful APIs, microservices, JSON/XML processing, and secure API design.
Tools & Platforms
Experience with GitHub/GitLab, JIRA, VS Code, IntelliJ, Postman, and cloud services (Azure/AWS).
Working knowledge of CI/CD tools and pipelines.
Soft Skills
Strong analytical reasoning, structured problem-solving, and excellent written communication.
Ability to collaborate with AI engineers, Java developers, business analysts, and product teams in multidisciplinary environments.
Adaptability to rapidly evolving AI technologies.
Preferred Experience (Optional)
Academic or internship work involving generative AI, NLP, chatbots, or ML experimentation.
Exposure to enterprise Java applications, Spring Boot microservices, or cloud-native architectures.
Experience with model evaluation, fine-tuning, or building vector search features.
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