Senior Lead - Gen AI Solutions Consultant Job at Wells Fargo (Bengaluru)

Job Description

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.

Job Responsibility

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

Requirements

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
Production‑Grade LLM Agent Deployment: Proven experience designing, deploying, and operating LLM‑driven agentic systems in production, with attention to reliability, safety, evaluation, and governance in enterprise environments
Deep LLM Platform Expertise: Hands‑on mastery of modern LLMs (GPT‑4/4.1, Claude, Gemini, or equivalents), including model selection, prompt‑to‑model matching, latency/cost optimization, and comparative evaluation
LLM Reasoning & Prompt Engineering: Advanced capability in prompt engineering, structured reasoning, chain‑of‑thought management, constrained generation, and output validation for real‑world workflows
Agentic LLM Architecture & Orchestration: Strong understanding of LLM‑based agent architectures, including tool calling, function schemas, multi‑agent coordination, planning loops, memory strategies, and guardrails
RAG, Context Injection & Knowledge Integration: Practical experience implementing RAG pipelines, embeddings, retrieval strategies, document chunking, re‑ranking, and contextual grounding to reliably augment LLM outputs
LLM‑to‑Enterprise Integration: Experience integrating LLM agents with enterprise systems using Python (APIs, databases, SaaS tools, internal services) via MCP or comparable LLM‑integration frameworks
Python‑Based Agentic Systems Engineering: Advanced Python proficiency for building production‑grade agentic systems including planning loops, state management, async execution, and modular service design
Customer‑Facing LLM Solution Ownership: Ability to work directly with customers and senior stakeholders to translate business processes into LLM‑powered 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

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Senior Lead - Gen AI Solutions Consultant

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