Senior Software Engineer - Gen AI

Job Description About this role: Wells Fargo is seeking a Senior Software Engineer

In This Role, You Will

• Lead moderately complex initiatives and deliverables within technical domain environments
• Contribute to large scale planning of strategies
• Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments
• Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures
• Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements
• Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals
• Lead projects and act as an escalation point, provide guidance and direction to less experienced staff

Required Qualifications

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

Desired Qualifications

• Proven experience designing and scaling enterprise GenAI platforms and AI-driven products.
• Strong expertise in Agentic AI, multi-agent systems, RAG architectures, Knowledge Graphs, and long-term memory frameworks.
• Experience architecting model orchestration solutions, including model routing, selection, fallback, and optimization strategies.
• Proficiency with GenAI frameworks including LangGraph, LangChain, AutoGen, MCP, or equivalent orchestration technologies.
• Deep understanding of LLMOps, AI platform engineering, CI/CD automation, and production-grade AI deployments.
• Hands-on experience with cloud AI platforms such as Azure OpenAI, AWS Bedrock, and Google Vertex AI.
• Strong proficiency in Python with experience building services using frameworks such as Fast MCP, FastAPI & Flask.
• Experience developing and integrating with REST APIs, asynchronous services, and event-driven architectures.
• Expertise in vector databases, graph databases, embeddings, semantic search, and retrieval technologies.
• Strong cloud-native engineering background with Kubernetes, microservices, containers, and distributed systems.
• Experience establishing AI observability, monitoring, evaluation, resiliency, and reliability practices.
• Knowledge of AI security, responsible AI, governance, privacy, and regulatory requirements.
• Understanding of token economics, AI cost optimization, usage forecasting, and platform efficiency strategies.
• Experience designing reusable APIs, SDKs, and developer frameworks that accelerate enterprise adoption.
• Experience in working on relation database like Oracle, MS SQL Server, etc.
• Experience with automated testing with Playwright, performance testing, code reviews, and CI/CD practices.
• Experience building secure, scalable, maintainable applications aligned with enterprise platform standards.

Job Expectations

• Lead moderately complex initiatives and deliverables within technical domain environments
• Design, build, and maintain AI-enabled applications using React, TypeScript, Python, and modern API frameworks.
• Develop  frontend applications aligned with enterprise UI standards and reusable component patterns.
• Build and maintain Python backend services, APIs, MCP services, and integration layers using frameworks such as MCP, FastAPI & Flask.
• Design, implement, and integrate with MCP servers and MCP clients that enable secure context sharing between models, tools, applications, and agent runtimes.
• Develop and support agentic workflows that execute complex, multi-step tasks using frameworks such as LangChain Deep Agents, Google ADK, or similar agent frameworks.
• Build skill-based and modular agents that decompose capabilities into reusable, composable, and versioned skills.
• Build reusable components, services, tool schemas, and integration patterns that can be shared across enterprise AI applications.
• Integrate logging, tracing, and observability frameworks, including Arize & Splunk compatible logging and agent observability tooling.
• Ensure applications and services are ready for deployment on enterprise container platforms such as OpenShift or Kubernetes.
• Collaborate with AI developers, platform engineers, product teams, and architecture partners to deliver robust and scalable AI s…