Senior Software Engineer II – Enterprise AI Consulting & Embedded Experts

Principal Financial Group•Charlotte, NC

12d•Hybrid

About The Position

We’re looking for a Senior Software Engineer II to join our AI Enablement product family within the Consulting & Embedded Experts team. In this role, you will operate as a trusted technical advisor and hands-on engineer, partnering directly with product and business teams to accelerate the safe, secure, and scalable adoption of AI across the enterprise. Rather than owning a single product, you will embed with teams, shape solutions, and help scale AI capability through reusable patterns, platforms, and accelerators aligned to enterprise strategy.

Requirements

8+ years of engineering experience, including building and scaling distributed systems
Experience working in complex, matrixed environments with multiple stakeholders
Strong ability to operate in ambiguity and move between strategy and hands-on delivery
Proven ability to influence without authority and drive adoption across teams
Excellent communication skills—able to translate between technical and business audiences

Nice To Haves

Experience building AI/ML or GenAI solutions in production environments
Familiarity with LLMOps, MLOps, evaluation frameworks, and AI guardrails
Experience with cloud platforms (e.g., AWS) and modern data/AI architectures
Background in solution architecture, consulting, or platform engineering
Experience building reusable frameworks, accelerators, or internal platforms

Responsibilities

Partner with product and business teams to identify and deliver high-impact AI use cases aligned to enterprise priorities
Translate ambiguous business problems into practical, production-ready AI solutions
Focus on delivering value aligned to measurable outcomes and ROI
Act as an embedded engineer/architect within delivery teams to accelerate AI solution design and implementation
Bridge the gap between central AI enablement capabilities and domain-specific business needs
Collaborate in a federated operating model, enabling teams while maintaining enterprise standards
Contribute to and leverage enterprise AI platforms, MLOps/LLMOps capabilities, and reusable accelerators
Create reusable patterns (e.g., RAG architectures, evaluation frameworks, guardrails) that reduce time-to-market and cost
Help drive critical mass adoption of AI platforms across engineering teams
Design AI solutions that meet enterprise standards for security, governance, and responsible AI
Implement guardrails, evaluation strategies, and monitoring to ensure trusted AI outcomes and customer protection
Champion sustainable engineering practices including reusability, scalability, and governance alignment
Assess emerging AI technologies and guide teams on when and how to adopt them responsibly
Help teams move up the AI maturity curve—from point solutions to enterprise-scale, embedded AI capabilities
Influence architecture decisions that enable long-term transformation, not just short-term delivery
Mentor engineers and product teams on AI best practices, architecture, and delivery approaches
Act as a force multiplier, enabling teams rather than becoming a bottleneck
Contribute to internal knowledge sharing, playbooks, and AI community building (e.g., demos, patterns, guidance)