[Remote] Principal Engineer - AI Engineering/AI Software Engineering/Applied AI
Note: The job is a remote job and is open to candidates in USA. FICO is a leading global analytics software company, helping businesses in 100+ countries make better decisions. As a Principal Engineer on their Applied AI team, you will design and build AI-powered software that transforms the platform's operations and maintains high standards of reliability and responsible AI governance.
Responsibilities
- Design and build production AI systems — including agents, RAG pipelines, and LLM-powered workflows — that integrate seamlessly into FICO's analytics and decision management platform
- Translate product requirements into technical designs, balancing model capabilities, latency, cost, and reliability against real-world business constraints
- Develop robust evaluation frameworks and benchmarks to measure quality, safety, and regression across LLM-based features, and use those signals to drive iterative improvement
- Drive end-to-end delivery of AI features, including prompt and context engineering, tool/function design, writing reusable and well-tested code, running offline and online evaluations, and communicating results to stakeholders
- Build and operate the application layer around foundation models: orchestration, tool use, memory, retrieval, guardrails, observability, and human-in-the-loop workflows
- Fine-tune, distill, and adapt open and closed foundation models when warranted, and align technical choices with FICO's product strategy and roadmap
- Optimize inference performance, throughput, and cost across the serving stack — including caching, batching, routing, and model selection strategies
- Apply modern AI engineering practices across heterogeneous infrastructure, from CPU-bound orchestration services to GPU-accelerated inference and training workloads
Skills
- 12+ years Software engineering experience, with a demonstrated track record of shipping complex, production-grade systems and building applications powered by LLMs or other foundation models
- Hands-on experience designing and deploying LLM-based features in production including prompt and context engineering, tool/function calling, agentic workflows, and evaluation-driven iteration
- Strong coding skills in Python and/or TypeScript, with experience using modern AI SDKs and frameworks (e.g., the Anthropic, OpenAI, or Google SDKs; LangChain, LlamaIndex, LangGraph; agent frameworks; MCP)
- Solid working knowledge of how foundation models behave in practice — including their capabilities and failure modes and experience with fine-tuning, distillation, or model adaptation when product needs warrant it
- In-depth knowledge of architectural patterns of production LLM systems, including orchestration, tool use, memory, guardrails, observability, caching, and cost/latency optimization
- Experience building offline and online evaluation pipelines for AI systems defining metrics, building eval sets, running A/B tests, and using signals to drive iterative improvement
- Strong problem-solving and communication skills, with the ability to mentor peers, influence technical direction, and collaborate effectively across engineering, product, and data science teams
- Masters degree/Phd in Computer Science, a related technical field, or equivalent practical experience
- Experience with embeddings and information retrieval; hands-on experience with Retrieval-Augmented Generation (RAG) architectures and vector stores (e.g., Pinecone, Weaviate, pgvector) is strongly preferred
- Advanced degrees and open-source contributions are a plus
Benefits
- An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.
- The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences.
- Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so.
- An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.
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