AI Architect

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AI Architect

Role Overview

As an AI Architect within the Transformation Office, you will serve as the technical engine for business change. You will design and execute an "intelligence layer" that operates across the enterprise, moving between high-level cloud posture strategy and low-level technical execution. Your focus is the rapid deployment of Agentic AI systems, LLM-powered applications, and Conversational Assistants that bridge the gap between legacy data and modern business needs.

Responsibilities

Architecture Execution: Design and deploy end-to-end architectures for Generative AI applications, LLM solutions, and Conversational Assistants that integrate seamlessly with business workflows.

Agentic System Development: Build and scale autonomous agents using AWS AgentCore and Azure AI Agent Service, enabling automated, multi-step reasoning and action cycles.

Cloud Strategy & Posture: Define and enforce the overall cloud posture for AI initiatives, ensuring high-level scalability and low-level security across AWS and Azure ecosystems.

Advanced RAG Design: Architect Retrieval-Augmented Generation (RAG) systems to ground LLMs in proprietary enterprise data, optimizing for accuracy, retrieval speed, and context window management.

Legacy Connectivity: Implement non-invasive patterns to ingest data from legacy systems (Mainframes, SQL Server, on-prem DBs), feeding AI agents and assistants without requiring core system changes.

Advanced Analytics Integration: Partner with teams using platforms like Palantir, Databricks, or Snowflake to leverage unified data ontologies for enhanced AI reasoning.

IT & Security Partnership: Act as the primary technical envoy from the Transformation Office to the central IT and Security departments, ensuring all AI designs meet enterprise-grade governance, networking, and IAM standards.

Data Science Integration: Apply Data Science principles to the architectural lifecycle-overseeing feature engineering, statistical validation of model outputs, and the alignment of agentic logic with data-driven predictive signals.

Qualifications

Education: Bachelor's degree in Computer Science, Data Science, or a related technical field is required. A Master's degree in AI, Machine Learning, or a quantitative discipline is highly desirable.

Experience: 8+ years in systems architecture, with at least 3 years specifically executing GenAI, RAG, and Agentic AI solutions in a complex corporate environment.

Agentic Mastery: Hands-on experience with AWS AgentCore and Azure AI Agent Service; deep familiarity with agentic frameworks (e.g., LangGraph, CrewAI).

Data Science Flavor: Strong ability to bridge the gap between Data Science and Architecture, ensuring that models are not just "plugged in" but statistically sound, validated for bias, and optimized for data quality.

Technical Stack: Mastery of Python, SQL, and PySpark; expert knowledge of vector stores (e.g., Pinecone, OpenSearch, or Azure AI Search).

Cloud & Platform Exposure: Proven track record in AWS and Azure. Functional exposure to advanced analytics platforms like Palantir Foundry/AIP is a major plus.

Transformation Mindset: Experience working in a Transformation Office or similar high-speed innovation hub, with a proven ability to partner with traditional IT to overcome infrastructure hurdles.

Execution Excellence: Demonstrated success in moving AI projects from high-level "Proof of Value" to low-level production-hardened systems.

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Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace.

If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.