Senior Lead - Gen AI Solutions Consultant
Wells Fargo · Hyderabad · onsite · Posted 69d ago
Undisclosed7–12 yrsfulltimeSourced from
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About this role
Job Summary
Wells Fargo is seeking a Senior Lead as Forward Deployed Agentic Engineer that will drive the Agentic AI products across the Chief Operating Office, acting as the bridge between product development, operations, and AI engineering. The role focuses on embedding LLM-powered, Agentic workflows directly into enterprise processes, enabling measurable business outcomes, operational transformation, and scalable AI adoption.
Responsibilities - Forward Deployment of Agentic AI Products: Lead end-to-end deployment of agentic AI solutions into live banking operations; Embed AI agents directly into operational workflows for automation and decision support; Work closely with business and operations teams to move from concept to production; Ensure enterprise-grade deployment, scalability, and governance of AI solutions.
- Discovery, Process Intelligence Value Realization: Conduct structured discovery sessions with business and operations stakeholders; Analyze process artifacts (SOPs, runbooks, workflows, video recordings, process mining outputs, etc.) to identify AI and automation opportunities; Translate operational processes into agentic workflows and solution blueprints; Define KPIs and quantify business benefits (cost reduction, cycle time, risk mitigation, productivity gains). Build ROI models and value realization frameworks for AI initiatives.
- Agentic Solution Design, MCP Tools Workflow Orchestration: Design and implement agentic AI solutions using process artifacts as input for workflow automation; Develop multi-step AI agents capable of reasoning, tool use, and task execution; Leverage MCP (Model Context Protocol) tools to integrate LLMs with enterprise systems, APIs, and data sources; Enable tool-augmented LLM workflows including function calling, orchestration, and structured outputs; Build reusable agent patterns for banking operations use cases (e.g., case resolution, reconciliation, compliance checks).
- LLM GenAI Expertise (Hands-on Execution): Hands-on experience with LLM platforms including: Claude (Anthropic) Gemini (Google) GPT (OpenAI); Design prompt architectures, reasoning chains, and agent workflows. Implement RAG (Retrieval-Augmented Generation) and contextual grounding strategies. Evaluate model performance, reliability, and enterprise readiness. Ensure responsible AI usage, governance, and compliance adherence.
- Capability Development: Agentic Skills Library Reusable Assets: Build and maintain an Agentic Skills Library of reusable capabilities, including: Task-specific AI agents (e.g., summarization, reconciliation, classification, decision support). Workflow components (e.g., data extraction, validation, routing, reasoning chains). Prompt templates, tool-use patterns, and orchestration blueprints. Enable systematic reuse of agentic capabilities across multiple banking use cases. Establish standards for versioning, governance, and lifecycle management of agent skills. Continuously expand the library based on production learnings and new use cases. Drive build once, reuse everywhere approach for agentic automation.
- Overall 18+ of industry experience with 7+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- Digital product management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, education
- ProductionGrade LLM Agent Deployment: Proven experience designing, deploying, and operating LLMdriven agentic systems in production, with attention to reliability, safety, evaluation, and governance in enterprise environments.
- Deep LLM Platform Expertise: Handson mastery of modern LLMs (GPT4/4.1, Claude, Gemini, or equivalents), including model selection, prompttomodel matching, latency/cost optimization, and comparative evaluation.
- LLM Reasoning Prompt Engineering: Advanced capability in prompt engineering, structured reasoning, chainofthought management, constrained generation, and output validation for realworld workflows.
- Agentic LLM Architecture Orchestration: Strong understanding of LLMbased agent architectures, including tool calling, function schemas, multiagent coordination, planning loops, memory strategies, and guardrails.
- RAG, Context Injection Knowledge Integration: Practical experience implementing RAG pipelines, embeddings, retrieval strategies, document chunking, reranking, and contextual grounding to reliably augment LLM outputs.
- LLMtoEnterprise Integration: Experience integrating LLM agents with enterprise systems using Python (APIs, databases, SaaS tools, internal services) via MCP or comparable LLMintegration frameworks.
- PythonBased Agentic Systems Engineering: Advanced Python proficiency for building productiongrade agentic systems including planning loops, state management, async execution, and modular service design.
- CustomerFacing LLM Solution Ownership: Ability to work directly with customers and senior stakeholders to translate business processes into LLMpowered agentic solutions, define success metrics, and deliver measurable impact.
- Ability to work on site per Wells Fargo's standard operating model in the listed location
- The ability to travel up to 10% of the time.
- Flexibility in shift timings to give global coverage
Sourced from naukri
Skills
data extractionAutomationclaudeapisclassificationCost ReductionDigital Product ManagementBankingdeploymentArchitecturecompliancebanking operationsCustomer RelationshipsAi SolutionsArtificial IntelligenceAnthropic SDK
About Wells Fargo
Wells Fargo
Financial Services
20k+
employees
1852
174 years old
Bengaluru/Bangalore, Karnataka
India
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Wells Fargo works to create positive social impact in the communities it serves by supporting housing affordability, small business growth, financial health, and a low-carbon economy.
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Financial Services
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