Level

Wells FargoPosted 3d ago

Lead Software Engineer - Python/Gen AI/RAG

Lead Software Engineer - Python/Gen AI/RAG at Wells Fargo scores 88 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

Hyderabad, IndialeadFull time

AI in this role

langchainlanggraphcrewaiautogen
prompt-engineeringragllm-integrationai-agentsai-safety

Job Description

About this role:

Wells Fargo is seeking a Lead Software Engineer


In this role, you will:

  • Lead complex technology initiatives including those that are companywide with broad impact
  • Act as a key participant in developing standards and companywide best practices for engineering complex and large scale technology solutions for technology engineering disciplines
  • Design, code, test, debug, and document for projects and programs
  • Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors
  • Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives
  • Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals
  • Lead projects, teams, or serve as a peer mentor
  • Lead projects, teams, or serve as a peer mentor


Required Qualifications:

  • 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education


Desired Qualifications:

  • 5 - 10+ years of overall software engineering experience, with strong hands-on Python expertise.
  • Proven experience building AI/Generative AI (GenAI) applications in production environments.
  • Strong understanding and hands-on experience with RAG (Retrieval-Augmented Generation) architectures — embeddings, vector databases, chunking strategies, retrieval optimization.
  • Practical experience building Agentic AI systems — multi-agent orchestration, planning, tool-calling, memory management (e.g., LangChain, LangGraph, AutoGen, CrewAI, DeepAgents, or similar).
  • Experience designing low-code/configuration-driven frameworks or platforms for non-technical users.
  • Solid understanding of LLM integration patterns (prompt engineering, function/tool calling, streaming responses via SSE/WebSockets).
  • Strong API design skills using frameworks like FastAPI/Flask, with experience in asynchronous programming (asyncio).
  • Experience with structured logging, observability, and resilience patterns (circuit breakers, retries) in distributed systems.
  • Strong problem-solving skills and ability to work in a fast-paced, evolving AI product environment
  • Experience with OpenShift/Kubernetes for container orchestration and platform deployment.
  • Familiarity with MongoDB or other NoSQL databases for state/checkpoint management.
  • Exposure to enterprise authentication/authorization patterns (AD/SSO, RBAC).
  • Experience with CI/CD tooling (GitHub Actions, Jenkins) and code quality tools (SonarQube).
  • Prior experience in financial services / capital markets domain.
  • Familiarity with MCP (Model Context Protocol) or similar tool-integration standards.

Job Expectations:

  • Architect and build a Valence (CIBX) agentic platform that allows business users to configure, deploy, and manage AI agents without deep technical expertise.
  • Design and develop deep agents capable of multi-step planning, tool use, memory, and autonomous task execution (e.g., using frameworks like LangGraph, DeepAgents SDK, or similar).
  • Build and optimize Retrieval-Augmented Generation (RAG) pipelines for grounding agent responses in enterprise knowledge bases and documents.
  • Integrate Generative AI / LLM capabilities (via internal or third-party model gateways) into scalable backend services.
  • Design orchestration layers for routing, planning, task execution, and result aggregation across multiple specialized agents.
  • Enable business teams to build agents for automation, analytics, insights, and predictive use cases with reusable templates and configuration-driven workflows.
  • Define and implement MCP (Model Context Protocol) or similar tool-calling protocols to connect agents with enterprise systems and APIs.
  • Ensure platform scalability, reliability, and observability (logging, monitoring, tracing) for production-grade agentic workloads.
  • Collaborate with product, architecture, and business stakeholders to translate BAU pain points into agent-driven automation solutions.
  • Mentor junior engineers and drive best practices in agentic system design, prompt engineering, and AI safety/guardrails.
  • Contribute to CI/CD pipelines, containerization, and deployment strategy for the platform.

Posting End Date: 

25 Sep 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.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRagLlm IntegrationAI AgentsAI SafetyLangChainLangGraphCrewAI

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. How have you integrated a large language model into a production application?
  4. How do you decide when an AI agent can act on its own versus asking for approval first?
  5. How do you think about the risk of an AI system in this kind of role failing silently?

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering, Rag, Llm Integration, AI Agents, and AI Safety. 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.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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