Lead Artificial Intelligence Solutions Consultant - Forward Deployed Agentic Engineer

• 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

5+ 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