Prompt Engineer & AI Applications Lead

Prompt Engineer & AI Applications Lead
(Madrid Spain orBengaluruIndia Hybrid/Flexible)

About the Role

Cigna is accelerating responsible AI adoption across the enterprise.Wereseeking aPrompt Engineer & AI Applications Leadto partner with product data engineering and business stakeholders to discover evaluate and scale AI use caseswhile ensuring safety compliance and measurable value. This role blends handson prompt design with business relationship management and vendor collaboration to drive outcomes across multiple time zones.

WhatYoullDo (Key Responsibilities)

AI Use Case Discovery & Shaping

• Identify qualify and prioritize highimpact AI opportunities across functions (operations clinical customer support finance IT).

• Facilitate discovery workshops to define problem statements success metrics guardrails and adoption plans.

• Translate business needs into AI solution patterns (e.g. retrievalaugmented generation summarization classification extraction assistants/agents).

Prompt Engineering & Experimentation

• Design test andoptimizeprompts chains and system instructions for performance safety and reliability across multiple LLMs and tools.

• Develop prompt evaluation frameworks (A/B tests golden sets bias & hallucination checks cost/latency tracking).

• Create reusable prompt templates style guides and prompt libraries for enterprise use.

Vendor & Partner Management

• Assess AI vendors for capability compliance total cost of ownership integration complexity and roadmap fit.

• Codevelop POCs/pilots with vendors; define SLAs acceptance criteria and transition plans to scale or sunset.

• Coordinate security privacy legal and procurement reviews; document decisions and risks.

Solution Delivery & Governance

• Partner with Data/ML Engineering to integrate RAG pipelines guardrails and observability (prompt logs redteaming results monitoring).

• Ensure adherence to responsible AI data privacy and healthcare compliance requirements (e.g. PHI handling HIPAAaligned processes in consultation with compliance teams).

• Contribute to model cards risk assessments and humanintheloop controls.

Stakeholder Enablement & BRM

• Serve as a trusted advisor to business leadersclarify value cases manage expectations and align roadmaps and budgets.

• Build enablement materials (playbooks demos FAQs) and deliver training for nontechnical and technical audiences.

• Communicate progress risks and ROI via clear narratives and dashboards.

WhatYoullBring (Qualifications)

MustHave

• 5 yearsof relevant experience in AI/ML product work datadriven solutions or technical consulting with at least2 years handson prompt engineering(ordemonstrableexpertise).

• Business Relationship Management (BRM)experience: stakeholder alignment requirements shaping roadmap facilitation and value realization.

• ProficiencywithLLMs and tooling(e.g. prompt design fewshot techniques function calling/tools vector search/RAG evaluation methods).

• Practical knowledge ofdata fundamentals(data quality governance metadata PII/PHI concepts) and safe AI patterns.

• Strong communication: can translate complex AI concepts for executives and nontechnical teams; excellent storytelling and documentation.

• Collaborative mindsetand experience workingacross multiple time zonesand crossfunctional teams.

• Bachelors degree in a relevant field (Computer Science Data/AI Engineering or related); equivalent experience considered.

• Location:MadridSpainorIndia(major hubs such as Bengaluru/Hyderabad/Pune preferred; flexible).

NicetoHave

• Experience inhealthcare/insuranceprocesses and analytics.

• Familiarity withevaluation frameworks(e.g. golden datasets rubricbased scoring)guardrails/redteaming andobservability.

• Exposure toMLOps/LangChain/LlamaIndex vector databases and API integration patterns.

• Practical understanding ofresponsible AIprinciples and risk controls; experience partnering withLegal/Privacy/Compliance.

• Comfort with one or more languages or tools (Python SQL REST APIs YAML) and with cloud platforms (Azure/AWS/GCP).

Success Metrics (KPIs)

• Validated use cases discoveredper quarter and percent advancing from POC Pilot Scale.

• Model quality gains(e.g. accuracy factuality reduction in hallucination) andexperience metrics(latency cost per task).

• Risk reduction(guardrail coverage incident rate redteam findings addressed).

• Stakeholder satisfaction(NPS/CSAT) andenablementreach(teams trained assets reused).

• Vendor outcomes(SLA adherence value realization cost savings/avoidance).

Ways of Working

• Hybrid work with core collaboration hours overlappingEuropean and India time zones; occasional evening/morning meetings to align with global teams.

• Limited travel for workshops or vendor evaluations as needed.

• Partner closely with Product Owners Data/ML Engineering Architecture Security Privacy Legal and Procurement.