Staff Software Engineer (Applied AI)

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

Hiring Team Description:

LinkedIn Learning Solutions Engineering India is a ground-up, AI-first org building scalable systems and consumer-grade experiences that connect skills, learning, career growth, and internal mobility across LinkedIn Learning and the enterprise Career Hub. Engineers own end-to-end delivery across web, mobile, backend, data, and AI - designing architectures for skills intelligence, skill-gap detection, personalized recommendations, workforce transformation, and internal mobility—while shaping engineering standards, culture, and hiring in a greenfield environment with LinkedIn’s scale.

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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