Principal Engineer - Java springboot, Gen AI, Agentic AI, LLM, RAG
Wells Fargo (company site, opens in a new tab)7+ yrsBengaluruOn-siteDirect career pageEst. 35–60 LPA
Posted on 8 Sept
Reach people at Wells Fargo
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Wells Fargo you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own foundational platform components integrating LLM-driven AI capabilities into high-throughput, mission-critical Java-based banking systems.
- Lead strategy and resolution of complex challenges across enterprise banking AI platforms, influencing architectural decisions and delivering scalable, secure solutions.
- Deploy production-grade GenAI applications, including RAG pipelines and agent orchestration layers, aligned with commercial banking product strategy and regulatory compliance.
Minimum Requirements
- 7+ years of engineering experience or equivalent demonstrated via work, training, military, or education.
- Strong hands-on expertise in Java ecosystem including Spring Boot, REST APIs, microservices, concurrency, and performance tuning.
- Experience delivering GenAI systems with LLMs, RAG architectures, embeddings/vector DBs, and agent frameworks (1-2 years preferred).
- Experience working within regulated financial services environments or banking domains not explicitly mentioned but implied.
Ideal Candidate Profile
- Deep expertise combining Java-based distributed systems engineering with modern GenAI architectures in regulated environments.
- Proven ability to lead AI platform design and architecture at scale, influencing complex systems and integrating AI into enterprise backend platforms.
- Experience integrating AI governance, risk, and model validation frameworks within banking or financial services contexts, with strong stakeholder engagement skills.