Principal Engineer – GenAI in SDLC (Java & Agentic AI)
Wells Fargo is hiring a Principal Engineer – GenAI in SDLC (Java & Agentic AI) in Bengaluru, India. Level rates it ; you can apply on Wells Fargo.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions OpenAI, Claude and Copilot among its tools and skills.
- Level classes it as a lead role.
- Wells Fargo has 1,500+ other open roles on Level.
- Posted in the last few days.
AI in this role
About this role:
Wells Fargo is seeking a Principal Engineer to lead the transformation of software engineering through AI-Augmented and Agentic Software Development. This role will define and drive the enterprise strategy, architecture, and implementation of Generative AI, AI Agents, AI Teammates, and Intelligent Automation across the Software Development Lifecycle (SDLC).
As a key technical leader, you will establish enterprise-wide engineering patterns that enable developers to leverage AI safely, effectively, and at scale. You will partner across Developer Platforms, Internal Developer Portal (IDP), CI/CD, Knowledge Foundation, and Agentic Engineering initiatives to accelerate software delivery, improve developer productivity, enhance engineering quality, and advance Wells Fargo's AI-first engineering transformation.
In this role, you will:
- Act as the enterprise technical authority for AI-Augmented SDLC, Agentic Engineering, and AI-Native Software Development practices.
- Define the technical vision, architecture, and roadmap for integrating Generative AI capabilities across the software development lifecycle.
- Lead enterprise-wide adoption of AI Agents, AI Teammates, Developer Assistants, and Intelligent Automation platforms.
- Establish reusable frameworks, standards, governance models, and reference architectures for AI-enabled engineering solutions.
- Design and implement autonomous and semi-autonomous AI Agents supporting software planning, development, testing, code review, vulnerability remediation, release automation, documentation generation, and knowledge discovery.
- Define Multi-Agent and Human-in-the-Loop architectures that balance automation, engineering productivity, and operational risk management.
- Build enterprise integrations leveraging Model Context Protocol (MCP), Knowledge Foundation platforms, retrieval systems, and developer productivity tooling.
- Drive the integration of Generative AI capabilities across Internal Developer Portals, CI/CD platforms, IDEs, developer workflows, and release pipelines.
- Establish self-service AI engineering capabilities that enable development teams to accelerate software delivery while improving quality and consistency.
- Develop enterprise observability, telemetry, adoption, and value realization strategies for AI-enabled engineering solutions.
- Partner with Security, Risk, Compliance, Architecture, and Technology leadership teams to establish responsible AI governance and controls.
- Influence engineering strategy across multiple technology organizations and serve as a trusted advisor to senior technology executives.
- Mentor Principal Engineers, Distinguished Engineers, Architects, and Engineering leaders while establishing enterprise standards for AI-Native Software Engineering.
Required Qualifications:
- 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- Strong Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
- 5+ years of experience architecting and building enterprise-scale distributed systems and cloud-native platforms.
- Extensive experience designing and scaling developer platforms, software engineering ecosystems, and enterprise engineering capabilities.
- Demonstrated success defining technology strategy and influencing architecture decisions across large, complex organizations.
- Strong expertise in Java-based engineering, microservices architecture, cloud-native application development, and modern software engineering practices.
- Experience implementing enterprise DevOps, CI/CD, platform engineering, and developer productivity solutions.
- Experience evaluating, implementing, or scaling AI-enabled engineering capabilities, developer assistants, or intelligent automation platforms.
- Strong leadership, stakeholder management, and enterprise architecture communication skills.
Desired Qualifications:
- Experience leading enterprise AI transformation, AI-Augmented SDLC, or Agentic Engineering initiatives.
- Deep understanding of Generative AI platforms, Large Language Models (LLMs), and AI application architectures.
- Experience with AI coding platforms including GitHub Copilot Enterprise, Devin AI, Claude Code, Cursor, or similar AI-assisted development tools.
- Expertise with Agentic AI frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, or OpenAI Agents Framework.
- Experience building Multi-Agent Systems, Human-in-the-Loop architectures, AI Teammates, and autonomous workflow orchestration solutions.
- Knowledge of Model Context Protocol (MCP), enterprise tool integration, context engineering, and developer workflow automation.
- Experience implementing Retrieval Augmented Generation (RAG), enterprise search, semantic search, and knowledge foundation capabilities.
- Experience with vector databases and AI retrieval platforms including Azure AI Search, Pinecone, Weaviate, Milvus, or similar technologies.
- Deep understanding of AI governance, AI observability, model evaluation, prompt engineering, agent evaluation, and responsible AI practices.
- Financial services or highly regulated industry experience preferred.
- Strong thought leadership demonstrated through enterprise transformation initiatives, technical publications, conference presentations, or industry contributions.
Job Expectations:
- Lead the enterprise roadmap and technical strategy for AI-Augmented SDLC and Agentic Engineering capabilities.
- Drive adoption and measurable business outcomes from AI-enabled engineering solutions across multiple technology organizations and business lines.
- Establish enterprise engineering standards, governance patterns, and operating models for AI-Native Software Development.
- Influence the future direction of Developer Experience, Platform Engineering, and Intelligent Software Delivery capabilities.
- Partner closely with senior technology leadership, risk, compliance, and security organizations to ensure responsible and scalable implementation of AI capabilities.
- Mentor and develop senior technical talent across Engineering, Architecture, and Platform organizations.
- Stay current on industry trends, emerging AI technologies, agentic frameworks, and modern software engineering practices.
- Ability to communicate complex technical concepts effectively to executive leadership, technical teams, and cross-functional stakeholders.
- Hybrid work model and occasional travel may be required based on business needs.
Posting End Date:
27 Oct 2026*Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
How we rate this
Principal Engineer – GenAI in SDLC (Java & Agentic AI) at Wells Fargo rates 72 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a workflow you automated with AI tools, end to end.
- How do you decide that one model's output is better than another's for a given task?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI agents, AI Automation, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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