Job Summary

Wells Fargo is seeking a Principal Engineer In this role, you will:

- Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups

- Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking

- Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions

- Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions

- Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization

- Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership

Required Qualifications

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

Desired Qualifications

- 7+ years of overall technology experience in software engineering, enterprise architecture, platform engineering, database technology, machine learning, or AI-led transformation roles.

- 7+ years of experience in architecture and technical leadership, including defining technology strategy, target-state architecture, roadmaps, standards, and reusable engineering patterns for large enterprise platforms.

- 3+ years of hands-on experience in AI/ML, Generative AI, or intelligent automation, including architecting and delivering production-grade AI solutions that create measurable business value with integrations to enterprise approved AI tooling

- Solid expertise in Generative AI, Agentic AI, Machine Learning, LLM-based applications, RAG architectures, AI agents, knowledge systems, prompt engineering,



model evaluation, and intelligent workflow automation.

- Proven experience designing and delivering enterprise-scale AI platforms and services, including reusable APIs, orchestration frameworks, developer toolkits, reference architectures, and common capabilities that accelerate adoption across teams.

- Deep understanding of cloud-native architecture, distributed systems, data platforms, API design, microservices, observability, scalability, reliability, and production deployment practices.

- Demonstrated experience partnering with executive leadership, business stakeholders, CIO organizations, architects, engineering teams, cybersecurity, risk, compliance, and governance partners to shape AI strategy and drive enterprise transformation.

- Strong knowledge of AI governance, responsible AI, model risk management, privacy, security, regulatory compliance, ethical AI principles, data protection, and operational risk controls.

- Experience evaluating and adopting modern AI technologies, frameworks, platforms, and vendors, including supporting build-vs-buy decisions, technology rationalization, cost optimization, scalability assessment, and total cost of ownership analysis.

- Ability to lead large, complex, cross-functional initiatives involving cloud platforms, data engineering, cybersecurity, enterprise applications, AI services, and automation platforms.

- Demonstrated ability to serve as a senior technical authority, providing architecture governance, design reviews, engineering guidance, mentoring, and thought leadership to senior engineers and architects.

- Excellent communication, influencing, and storytelling skills with the ability to translate complex AI concepts into clear business outcomes, architecture decisions, risks, and investment recommendations.

- Good understanding of database platforms like Oracle, PostgreSQL, MongoDB

Job Expectations

- Advanced degree in Computer Science, Artificial Intelligence,



Machine Learning, Data Science, Engineering, Mathematics, or a related technical discipline.

- Experience building and deploying solutions using modern AI/ML and GenAI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, Hugging Face, MLflow, TensorFlow, PyTorch, or similar technologies.

- Hands-on experience with cloud AI services and platforms such as Devin AI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, Snowflake, Kubernetes, OpenShift, or other enterprise cloud platforms.

- Experience implementing agent orchestration patterns, multi-agent systems, workflow automation, tool/function calling, memory management, contextual retrieval, and human-in-the-loop controls.

- Strong understanding of LLMOps/MLOps practices, including model lifecycle management, prompt/version management, evaluation pipelines, guardrails, monitoring, drift detection, observability, and automated deployment.

- Experience designing AI solutions for highly regulated environments, preferably in financial services, banking, healthcare, insurance, or other risk-sensitive industries.

- Working knowledge of cybersecurity architecture, identity and access management, secrets management, data classification, encryption, secure SDLC, vulnerability management, and threat modeling for AI platforms.

- Experience with enterprise knowledge management, semantic search, vector databases, embeddings, metadata enrichment, data lineage, and retrieval optimization.

- Proven ability to establish engineering standards, architecture guardrails, reusable design patterns, coding best practices, and governance models for AI adoption at enterprise scale.

- Experience leading innovation initiatives, proof of concepts, technology incubations, hackathons, or AI transformation programs that move from experimentation to production adoption.

- Strong vendor management experience, including evaluating AI platforms, third-party tools, licensing models, commercial terms, platform fit, operational readiness, and long-term maintainability.

- Recognized thought leadership through whitepapers, internal architecture forums, patents, publications, conference talks, technical communities, or enterprise-wide engineering leadership.

📌 Principal Engineer (AI) For Database Technology (Bengaluru)
🏢 Wells Fargo
📍 Bengaluru