Staff Software Engineer (Applied AI)

LinkedIn Learning is entering a significant new phase of growth and innovation. What started as a learning platform has evolved into a much broader vision: helping organizations understand, develop, and transform workforce capabilities at scale. Our new Learning Solutions Engineering charter is going to be built ground-up from India exclusively and is building AI-powered experiences that connect skills, learning, career growth, and internal mobility.

The Learning Solutions Engineering team is being built to drive this next chapter. This is a rare opportunity to join a business that already serves millions of professionals globally while helping shape the future of AI-powered workforce transformation.

About the Role:

We're looking for a Staff Engineer to lead the design and delivery of AI-native systems that solve real product and business problems. You'll set technical direction for how we apply LLMs and agentic systems at scale , from prototyping to production-grade, evaluated, and observable systems.

What You'll Do

• Design and build agentic workflows that orchestrate multi-step reasoning, tool use, and decision-making across complex tasks
• Architect and scale Retrieval-Augmented Generation (RAG) pipelines — including chunking, embedding, indexing, and retrieval strategy — for accuracy and latency at production scale
• Own prompt engineering practices: design, iterate, and systematize prompts across use cases, balancing reliability, cost, and latency
• Build robust evaluation frameworks (offline and online) to measure model/agent quality, catch regressions, and guide iteration
• Partner closely with product, data science, and infra teams to translate ambiguous problems into scoped AI-powered solutions
• Drive technical decisions on model selection, fine-tuning vs. prompting trade-offs, and build-vs-buy for AI infrastructure
• Mentor senior and mid-level engineers; raise the technical bar for applied AI practices org-wide
• Stay current with the fast-moving LLM/agent ecosystem and pragmatically bring relevant advances into the roadmap

What We're Looking For

• BS Degree in Computer Science or related technical discipline or related practical experience.
• 8+ years of software engineering experience, with 2+ years hands-on building applied AI/LLM-based systems in production

Nice to Have

• Experience with fine-tuning or custom model training
• Familiarity with LLM observability/tracing tools .
• Experience in a high-scale consumer or enterprise product environment
• Demonstrated experience designing and shipping agentic workflows (multi-step tool-calling, planning, orchestration frameworks like LangGraph, custom agent loops, etc.)
• Strong grasp of RAG system design — retrieval quality, embedding strategies, vector DBs, hybrid search
• Deep experience with prompt engineering methodologies and prompt/version management at scale
• Proven track record building evaluation systems for LLM outputs (automated evals, human-in-the-loop review, regression suites, LLM-as-judge techniques)
• Solid fundamentals in distributed systems, APIs, and production-grade software engineering
• Excellent communication skills; able to explain AI system trade-offs to both technical and non-technical stakeholders

Suggested Skills:

• LLM
• Backend Development
• Distributed Systems

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