Looking for Prompt Engineer || Jersey City, NJ / New York, NY || Fulltime

Hi,

Hope you are doing well.

We do have a position for Prompt Engineer with our client. Please find the details below and if interested please send me your updated word format resume.

Tittle: Prompt Engineer

Location: Jersey City, NJ / New York, NY

Duration: Fulltime

Experience : 4-6 Years

Must Have Technical/Functional Skills:

We are looking for experienced Prompt Engineers with strong hands-on expertise in Large Language Models (LLMs) to design, optimize, and operationalize high quality prompts for enterprise-grade GenAI solutions.

The role demands deep understanding of model behaviour, prompt patterns, and real-world deployment in regulated environments.

• 4 7+ years overall experience, with 2 3+ years hands-on Prompt Engineering / GenAI work.
• Strong experience with LLMs (GPT 4 / GPT 4o / Gemini / Claude / LLaMA or equivalent).
• Hands-on knowledge of RAG frameworks, embeddings, vector databases, and semantic search.
• Experience with Azure OpenAI / OpenAI APIs / Google Vertex AI / Hugging Face.
• Strong NLP fundamentals and ability to interpret LLM behaviour.
• Programming experience in Python (Fast API / Flask exposure preferred).
• Experience using LangChain / LlamaIndex or similar orchestration frameworks.
• Strong analytical skills to evaluate and improve model output quality.

Roles & Responsibilities:

• Design, test, and optimize advanced prompts (zero-shot, few-shot, chain-of-thought, tool calling, RAG-based prompts) for LLM-driven applications.
• Analyse model outputs and continuously refine prompts to ensure accuracy, consistency, and business relevance.
• Collaborate with Data Scientists, ML Engineers, and Product teams to integrate prompts into end-to-end AI workflows.
• Support RAG architectures by crafting effective prompts leveraging vector search, embeddings, and enterprise knowledge sources
• Implement guardrails, validation strategies, and hallucination mitigation techniques.
• Fine tune prompt strategies based on domain-specific use cases (knowledge extraction, summarization, classification, Q&A, reasoning tasks).
• Document prompt patterns, best practices, and reusable templates for enterprise adoption.
• Support client demos, POCs, and pilot rollouts of GenAI solutions.

Bhakti Chaudhari