Principal Engineer - AI / ML

About this role:

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:

• Bachelors or masters degree in computer science engineering, or a related technical field.
• Overall 16+ years of experience in software development skills, particularly in Python, with experience working with AI frameworks (like: LangChain, LangGraph).
• 5+ years of experience in ML, NLP, Information Retrieval, Recommender Systems and LLMs.
• Experience in Generative AI tech like Retrieval-Augmented Generation (RAG) design end-to-end pipelines (Vector DBs, chunking, embedding, retrieval, context injection) to ground LLM responses on enterprise knowledge.
• Ability to own the end-to-end AI/ML lifecycledesign, build, deploy, and maintain scalable agent solutions.
• Embed Generative AI and Agentic AI into day-to-day business processes to lift efficiency and employee productivity
• Experience in provisioning, operating, and maintaining systems running in the cloud environments (GCP, Azure)
• Deep understanding of microservice architecture, cloud-native platforms, and infrastructure-as-code.
• Expertise in Ansible, CI/CD pipelines, observability tools (e.g., Prometheus, Grafana, ELK, Datadog), and incident management frameworks.
• Familiarity with Enterprise level AI and automation tooling.
• Strong understanding of governance, compliance, and operational risk management in engineering environments.
• Ability to spot emerging tech trends and drive Proof of Concepts or pilot solutions
• Must be able to work on multiple projects and consistently meet project deadlines
• Interpersonal and communication skills, able to build rapport and communicate effectively at all levels and across disciplines
• Prior knowledge of working with enterprise platforms and building solutions, in consideration to enterprise-wide guidelines & standards

Job Expectations:

• We are open for both the locations - Hyderabad or Bangalore and will be required to work in the office as per organizations In Office Adherence / Return To Office (RTO)
• This is critical strategic role as part of I&O Automation (L3 Product) within ITSO. This position will be responsible for owning overall architecture and design of Product''s 1) application footprint (infrastructural and stack), 2) complex capabilities, 3) posture on "governance and risk controls" & 4) resiliency, stability, scalability improvements. Key expectations from the role include:
• Architecture/Design: This position is responsible for owning overall architecture and design of 1) Application footprint (infrastructural and stack), 2) complex capabilities, 3) Platforms posture on "governance and risk controls" & 4) resiliency, stability, and scalability improvements.
• Influence Strategic Pillars/Change the Bank: The position is critical to lead and drive bank''s digital transformational journey leveraging newer technologies like AI (Gen AI, Predictive Ai, Conversational Ai, Agentic Ai), & ML. to enable multiple 2025 (& beyond) strategic innovations like: Predict & prevent infra/application failures leveraging historical data insights using Predictive analytics , Gen- AI for automated identification & resolutions of user/infra/app issues, building standard automation patterns (Certificate Renewal, Automated incident remediation) & platform migration to Open Shift / New Data Centre & integrating with multiple enterprise wide approved platforms/technologies.
• Cross-Functional Enablement: Acts as a technical bridge between engineering, product, research, and compliance teams, ensuring that automation solutions are not only technically sound but also aligned with business goals and regulatory standards. Mentors senior engineers and data scientists, elevating the technical maturity of the organization and fostering a culture of innovation and responsible AI development. The role also plays a key role in removing key architectural bottlenecks of global teams and federated customers, by proposing innovative scalable, resilient, and secure design patterns & practices.
• Organizational Scope: While the role may not have direct people management responsibilities, it exerts broad technical leadership across multiple teams and lines of business. Influences the performance and deliverables of cross-functional groups by setting architectural standards, defining best practices, and guiding long-term AI capability roadmaps.
• Technical Breadth and Depth: The role spans multiple advanced domainsautomation orchestration, conversational, generative & Agentic AIeach of which demands specialized knowledge. Designing scalable, reusable solution architectures and pipelines involves navigating trade-offs between performance, cost, latency, and maintainability across diverse environments (cloud, on-prem, hybrid).