What are the responsibilities and job description for the Prompt Engineer position at The Value Maximizer?
Role Description: LLM Prompt EngineerLocation: Basking Ridge NJ About the RoleWe are seeking a highly creative and technically strong Prompt Engineer to design, optimize, and scale LLM-driven solutions within an Agentic POD architecture. The ideal candidate will have hands-on experience in LLM optimization (Gemini, Claude, Copilot), prompt engineering patterns, and Retrieval-Augmented Generation (RAG), along with a focus on improving engineering throughput and automation.Role and responsibilities 1. Design, develop, and optimize prompts and prompt chains for LLM-based applications2. Work within Agentic POD architectures to enable seamless interaction between AI agents3. Optimize performance of LLMs such as Gemini, Claude, GitHub Copilot, and other foundation models4. Develop and implement prompt patterns (few-shot, chain-of-thought, ReAct, tool-augmented prompting)5. Build and enhance RAG (Retrieval-Augmented Generation) pipelines for accurate and context-aware responses6. Create reusable prompt templates and frameworks for enterprise-scale applications7. Improve engineering throughput by leveraging AI-assisted development workflows8. Evaluate and fine-tune prompts using LLM evaluation frameworks and metrics (accuracy, latency, cost)9. Collaborate with AI engineers, data scientists, and product teams to deliver optimized solutions10. Ensure responsible AI practices, bias mitigation, and prompt safety controls11. Continuously experiment with new prompting techniques and LLM capabilities Technical skills requirements The candidate must demonstrate proficiency in,B.Tech., M.Tech. or MCA degree from a reputed university
- Strong understanding of LLMs and prompt engineering techniques· Hands-on experience with LLM optimization (Gemini, Claude, Copilot, OpenAI models, etc.)
- Expertise in prompt patterns:
- Few-shot / zero-shot prompting· Chain-of-thought reasoning· ReAct / tool-based prompting· Experience with RAG architectures (retrievers, embeddings, vector databases)
- Ability to design scalable prompt templates and reusable frameworks· Strong analytical skills for evaluating and improving model outputs· Familiarity with Python for prototyping and integrationNice-to-have skills
- Experience with LangChain / LangGraph / AutoGen· Familiarity with vector databases (FAISS, Pinecone, Weaviate)
- Knowledge of cloud platforms (GCP preferred - Vertex AI, AI Studio)
- Understanding of workflow orchestration (Airflow / Cloud Composer)Qualifications
- Overall 4 years with 2-7 years of relevant work experience in Agentic AI Development