Prompt Engineer
- Digital Aptech
- 4 - 9 years
- Kolkata
- 17 days ago
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Job Description
Role & responsibilities
Building a Prompt Engineers Team requires a thoughtful approach that balances technical expertise, domain knowledge, and creativity. Heres a step-by-step plan to help you build an effective team:
Step-by-Step Guide to Building a Prompt Engineering Team
1. Define the Purpose and Scope
Start with clarity on:
- Why you need a prompt engineering team (e.g., to improve chatbot accuracy, optimize RAG pipelines, generate content, automate processes).
- Where prompts will be applied (e.g., customer support, legal research, code generation, marketing content).
- What models/tools youre using (e.g., OpenAI GPT-4, Claude, Mistral, LangChain, LlamaIndex, Pinecone, etc.).
2. Identify Key Roles to Hire
You dont need only prompt writers build a balanced team. Typical roles include:
Core Prompt Engineers
- Focused on crafting, testing, and refining prompts
- Excellent with language, logic, and experimentation
NLP/LLM Engineers
- Technical experts who understand model internals, fine-tuning, embeddings, and vector search
- Useful if youre building complex RAG or multi-agent systems
Creative Writers or UX Writers (Optional)
- Can write natural, engaging, or persuasive prompts especially for customer-facing apps or content generation
Evaluation & QA Specialists
- Test prompt outputs for accuracy, bias, safety, and relevance
- Help benchmark performance
3. Create a Hiring Strategy
Look for Candidates Who:
- Understand LLM behavior and limitations
- Have experience with OpenAI, Claude, or open-source models
- Are comfortable with both creative writing and logical reasoning
- Can iterate based on output quality
Where to Find Them:
- AI/NLP communities on Twitter, Discord (e.g., EleutherAI, LangChain)
- LinkedIn with filters like: Prompt Engineer, LLM Developer, NLP Engineer
- Platforms like GitHub (search AI prompt repositories), Kaggle, Upwork, Toptal
4. Set Up Your Tech Stack
Equip the team with:
- LLM Access: OpenAI API, Anthropic, HuggingFace, Cohere
- Prompt Testing Tools: PromptLayer, LangSmith, PromptPerfect, Flowise
- RAG Stack (if needed): LangChain, LlamaIndex, Chroma, Weaviate, Pinecone
- Version Control: GitHub + prompt libraries
- Prompt Analytics/Logging: To track changes, feedback loops, hallucinations, and success rates
5. Define a Workflow
- Prompt Design Test Evaluate Refine Deploy Monitor
- Use prompt templates (Few-shot, Chain-of-Thought, ReAct, etc.)
- Implement CI for prompt validation if prompts are part of codebase
6. Build a Knowledge Base
Document:
- Prompt templates and examples
- What works well (prompt tuning guides)
- Failure cases and anti-patterns
- Team learnings, A/B test results, and prompt libraries
7. Encourage Experimentation
LLMs are probabilistic experimentation is critical. Create a culture where:
- Engineers test various prompt formats and model behaviors
- Insights are shared internally
- Prompt quality is benchmarked over time
Preferred candidate profile
Bonus: Team Composition Example (Startup Scale)
Role Count Notes
Prompt Engineer 2 NLP-aware, skilled with prompt design & LLMs
NLP/LLM Developer 1 Can build pipelines, evaluate embeddings, tune models
QA / Evaluator 1 Reviews and scores AI output for relevance, tone, accuracy
Creative Writer (Optional) 1 Especially useful for marketing, support, or storytelling prompts
Final Tip:
Your first hires should be T-shaped people with deep LLM experience but broad enough to collaborate across engineering, product, and UX.
Job Classification
Industry: Emerging Technologies (AI/ML)Functional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: Digital AptechLocation(s): Kolkata
Keyskills: Prompt Engineering Open Ai Large Language Model Artificial Intelligence Natural Language Processing Data Science R Aiml LLM Cohere Python
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