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Senior Specialty Software Engineer - Gen AI

Wells Fargo

Greater Hyderabad Area

About This Role

Wells Fargo is seeking a Senior Software Engineer

In This Role, You Will

Lead or participate in complex initiatives on selected domains
Assure quality, security and compliance for supported systems and applications
Serve as a technical resource in finding software solutions
Review and evaluate user needs and determine requirements
Provide technical support, advice, and consultation with the issues relating to supported applications
Create test data and conduct interfaces and unit tests
Design, code, test, debug and document programs using Agile development practices
Understand and participate to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives
Conduct research and resolve problems in relation to processes and recommend solutions and process improvements
Assist other individuals in advanced software development
Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals

Required Qualifications

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

Desired Qualifications

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.

Job Expectations

10–15 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.

Key Responsibilities

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.

Required Skills & Experience

10–15 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.

Good to Have

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.

Posting End Date

12 Nov 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

Third-Party recordings are prohibited unless authorized by Wells Fargo.
Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
Please be prepared to provide accurate information and supporting documentation in connection with any lawful employment and education verification conducted by Wells Fargo.

Reference Number

R-586124

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