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Prompt Engineer
NTT DATA, Inc.
Jersey City, USA
Full-time
Senior · 8+ years experience
Hybrid
USD 75-85 hourly / hour
Discovered 1 weeks ago
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Full Job Posting
- Design, test, govern, and continuously improve prompts, system instructions, conversation flows, and interaction patterns for AIRP LLM applications and related citizen-development experiences. The role ensures model outputs are accurate, grounded, safe, consistent, cost-aware, and aligned with business and compliance expectations.
- Client-specific emphasis
- Prompt work must support enterprise business use cases, not generic chatbot experimentation.
- Reusable prompt patterns should be suitable for AIRP and, where applicable, Copilot Studio / Power Platform citizen-development scenarios.
- Candidates must understand prompt security, sensitive data handling, citations/grounding, and structured evaluation.
- Primary ownership
- Prompt patterns, system instructions, response templates, and conversation policies for AIRP LLM use cases.
- Prompt testing, versioning, evaluation, and quality-improvement workflows.
- Reusable prompt libraries and guardrail patterns for business teams and responsible citizen development where applicable.
- Design prompts for chatbots, copilots, RAG systems, document analysis, summarization, workflow agents, knowledge assistants, and decision-support experiences.
- Develop system prompts, few-shot examples, tool-use instructions, response formats, escalation logic, citation behavior, and conversation policies.
- Optimize prompts for KYC support, credit underwriting support, governance tracking, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening use cases.
- Build reusable prompt libraries and templates aligned to enterprise standards, business domains, AIRP patterns, and citizen-development guardrails.
- Evaluate prompt performance using metrics such as task success, groundedness, hallucination rate*** completeness, safety, user satisfaction, latency, and token cost.
- Partner with engineers to implement prompt versioning, testing, deployment, and monitoring in production systems and CI/CD workflows.
- Support RAG quality by assessing retrieval context, chunking quality, source citation behavior, response synthesis, and missing-context behavior.
- Conduct adversarial testing for prompt injection, jailbreaks, instruction conflicts, sensitive-data leakage, unsafe outputs, and unauthorized tool use.
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