About this role:
- Wells Fargo is seeking a Lead Software Engineer.
- The COO Technology group provides technology services for the Chief Operating Office. This includes operations, control executives, strategic execution, business continuity and resiliency, data solutions and services, regulatory relations, customer experience, enterprise shared services, supply chain management, and the corporate properties group. COO Technology provides technology solutions and manages application
portfolios for these groups to support modernization and optimization. - Within COO Technology we are seeking a Lead Software Engineer - Generative AI - whose role is essential for executing strategic vision and driving concrete results
- The Generative AI Team is building next-generation autonomous workflow automation that transforms business processes through intelligent agent orchestration, knowledge extraction, and real-time observability. We're seeking a Lead Software Engineer - Generative AI to architect complex agentic systems, build scalable ETL pipelines, and mentor team members on GenAI best practices.
- Our platform's unique value lies in:
- Agentic AI Orchestration: Multi-agent workflows using Google ADK (Sequential, Parallel, Loop patterns) with built-in validation
- Modern ETL Pipelines: Data transformation for video content clustering and knowledge extraction
- Knowledge Graph Intelligence: Graph databases with semantic embeddings for intelligent task replication
- LLM Framework Integration: Google Gemini, LiteLLM routing, LangChain/LlamaIndex orchestration with prompt caching and function calling
- Observability-First Design: Real-time metrics, correlation tracking, and audit trails via OpenTelemetry, Splunk, or Arize Phoenix
- In this role, you'll own end-to-end implementation of agentic AI features, establish patterns for knowledge extraction ETL pipelines, and help define technical standards across the team.
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:
- 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Additional Required Qualification:
- 5+ years of Software Engineering experience with 2+ years in GenAI/AI systems implementation
- 2+ years hands-on experience with Python 3.12+ and modern AI/ML frameworks:
- LLM Frameworks: LangChain, LlamaIndex, or AutoGen
- Agent Platforms: Google ADK, Crew AI, or similar
- LLM APIs: Google Gemini, OpenAI, Claude, or LiteLLM
- 2+ years designing and implementing multi-agent systems or agentic AI workflows
- 2+ years direct hands-on experience with Large Language Models: LLM selection, prompt engineering, model routing, parameter tuning, function calling
- Experience with ETL pipeline development: data transformation, validation, orchestration
- Experience with semantic search and embeddings: vector databases (Pinecone, Weaviate, ChromaDB), embedding models, similarity search
- Experience with clustering algorithms (K-means, DBSCAN, hierarchical clustering) and pattern recognition
- Cloud platform experience (Google Cloud, AWS, or Azure) with focus on AI/ML services
- 3+ years with CI/CD/DevOps tools: Git, automated testing, deployment pipelines, container basics
- Experience with async Python patterns: asyncio, async/await, concurrent patterns
- Strong experience with database design: SQL, NoSQL, graph databases, repository patterns, transaction management
- Strong problem-solving skills with attention to detail and quality
Desired Qualifications:
- Software Engineering
- Python 3.12+ with async/await, type hints, modern frameworks
- Database Design: SQL, NoSQL, graph databases, transaction management, ORM frameworks
- Observability & DevOps
- Observability Architecture: OpenTelemetry, Splunk, Arize Phoenix, Prometheus metrics
- CI/CD & Deployment: Automated testing, deployment pipelines, container orchestration basics
- Cloud Platforms: Google Cloud, AWS, or Azure (AI/ML services focus)
- Quality & Security
- Test-Driven Development: Contract testing, integration testing, unit testing
- Secure Development: Data redaction, secure credential management, audit logging
- Performance Optimization: Inference tuning, ETL performance profiling, cost optimization
- Preferred Experience Level
- 3-5 years at mid-level positions (Senior IC roles) with architecture exposure
- Deep technical expertise with ability to own complex features end-to-end
- Self-sufficient in implementation with minimal guidance
- Mentorship capability with proven track record of helping junior engineers grow
- Communication skills to explain complex concepts to technical and non-technical stakeholders
Job Expectations:
- Technical Implementation & Ownership
- Design and implement multi-agent workflow systems using Google ADK supporting complex business processes (sequential execution, parallel branches, iterative loops)
- Own end-to-end implementation of GenAI features, from architecture to production deployment
- Build robust agentic AI systems using Python 3.12+, Google ADK, LangChain/LlamaIndex, and modern frameworks with strong testing discipline
- Architect ETL pipelines using LLMs for transforming raw video content into structured knowledge representations
- Implement semantic search and clustering using Scikit-learn, FAISS, or Pinecone for workflow pattern identification
- Build knowledge graph systems for capturing task dependencies and semantic relationships
- Optimize LLM inference with prompt caching, function calling, batch processing, and LiteLLM routing strategies
- Implement secure CI/CD pipelines for AI model deployment with comprehensive automated testing for agent behavior validation
- Architect and integrate observability instrumentation into agent execution lifecycle
- Optimize AI inference performance, manage costs, and tune model parameters (temperature, top_p, seed) for production workloads
- ETL Pipeline & Data Engineering
- Design scalable ETL pipelines for video metadata extraction, clustering, and knowledge graph construction
- Build feature engineering pipelines for embedding generation
- Implement streaming data pipelines with Apache Kafka or Redis for real-time knowledge updates
- LLM Integration & Prompt Engineering
- Architect LLM routing systems using LiteLLM with fallback patterns and cost optimization
- Implement prompt caching strategies for efficient API usage across multi-turn conversations
- Build function calling frameworks for agent tool invocation with proper type validation
- Integrate RAG (Retrieval Augmented Generation) systems with knowledge graphs and vector databases
- Implement chain-of-thought prompting and multi-turn conversation management with LLMs
- Design output validation pipelines using structured outputs and LLM guardrails
- Architecture & Design Decisions
- Lead technical design discussions for agentic AI features and knowledge extraction pipelines
- Evaluate and recommend AI frameworks: Google ADK, AutoGen, LangChain, LlamaIndex, Crew AI
- Design scalable database schemas and knowledge graph models
- Contribute to architectural decisions ensuring reliability, security, and scalability
Posting End Date:
19 Jul 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.
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To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
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