Associate Principal - Architecture

GenAI Solution Architect – Agentic Engineering

Role Overview

We are seeking a seasoned Application Architect who has developed strong, hands-on expertise in Generative AI. The ideal candidate continues to write code, has deployed agentic and GenAI solutions in production, and can translate AI-led engineering strategy into executable delivery models for customer stakeholders across multiple time zones.

Key Responsibilities

1. GenAI Solution Architecture and SDLC Acceleration

·Define the target-state architecture and adoption roadmap for an AI-led software development lifecycle, specifying where and how AI agents participate in planning, coding, review, testing, and deployment.

·Extend agentic workflows into engineering design and specification-driven development, using specifications and design documents as sources of truth and introducing AI-assisted architecture documentation and agent reviewers at design and code-review gates.

·Architect end-to-end GenAI solutions, including agentic developer workflows, retrieval-augmented generation pipelines, and large language model applications for insurance workloads; scale successful patterns from pilot to production.

·Lead AI-accelerated architecture transformation across core and legacy modernization and new event-driven architecture implementations. Apply agents to code comprehension, documentation, migration, functional-equivalence test generation, and event schema and handler scaffolding.

·Design AI-enabled business workflows with human-in-the-loop controls, including document intake and extraction, confidence thresholds, exception routing, reviewer feedback loops, and end-to-end auditability.

·Coach offshore development squads in agent-assisted engineering and establish working practices, quality gates, and guardrails for AI-generated code.

·Lead GenAI engineers by setting technical direction, reviewing designs, and building the team’s agentic engineering capability.

·Define, measure, and report acceleration outcomes, including cycle time, throughput, quality, and adoption metrics; continuously improve the delivery blueprint based on evidence.

·Establish and enforce architecture standards, design patterns, responsible AI practices, and governance controls aligned with insurance data privacy, security, and regulatory requirements.

·Build evaluation and guardrail capabilities as first-class engineering components, including evaluation harnesses, golden datasets, hallucination and regression checks, output safeguards, and production monitoring.

2. Agentic Engineering and Tooling

·Design multi-agent systems with clearly defined orchestrator, coder, reviewer, and tester roles, supported by task routing, state management, and human-in-the-loop checkpoints.

·Configure and govern enterprise GenAI developer tooling, including agentic coding tools, command-line coding agents, AI pair-programming solutions, MCP tool servers, custom commands, hooks, repository context and instruction files, agent SDKs, and orchestration frameworks.

·Design and deploy Model Context Protocol servers that securely expose customer tools, APIs, and data sources to AI agents, using appropriate tool-use and function-calling patterns.

·Select and integrate foundation models such as Anthropic Claude, OpenAI GPT, Gemini, Llama, and Mistral through APIs or managed platforms including AWS Bedrock, Azure OpenAI, Azure AI Foundry, and Google Vertex AI.

·Design model-tiering and routing strategies that balance capability, data residency, cost, latency, and workload volume.

·Deploy and operate GenAI workloads within enterprise cloud environments using private endpoints, network isolation, identity and access management, quota and rate-limit controls, regional-availability planning, model gateways, token budgeting, caching, and chargeback mechanisms.

3. Context Engineering, Business Ontology, and Domain Knowledge

·Mine legacy code, documentation, wikis, and tickets to establish a customer knowledge fabric, with curation, versioning, and freshness practices that keep context accurate as systems and regulations evolve.

·Prepare data for AI by profiling and cleansing source data, normalizing and deduplicating content, correcting OCR noise, structuring unstructured information, and curating versioned datasets, including synthetic data where appropriate.

·Protect data throughout AI pipelines through de-identification, PII masking, redaction, lineage, and role-based access controls for prompts, retrieval corpora, evaluation datasets, and fine-tuning data.

·Establish metadata management and data-governance practices, including business glossaries, data catalogs, lineage, and traceability for ontologies, retrieval corpora, and datasets.

·Apply disciplined prompt and context engineering techniques, including few-shot examples and structured reasoning approaches, to improve solution reliability and consistency.

Required Experience and Capabilities

·Strong application architecture background combined with recent, hands-on GenAI engineering experience.

·Demonstrated experience deploying agentic or GenAI solutions into live, enterprise environments.

·Ability to remain hands-on with coding while providing architecture leadership and technical governance.

·Experience translating AI strategy into practical delivery models for distributed customer and engineering teams.

·Working knowledge of multi-agent design, RAG, LLM applications, MCP, model platforms, cloud deployment, responsible AI, evaluation, monitoring, and data governance.

·Experience operating within privacy-sensitive and regulated environments; insurance-domain experience is strongly relevant.

·Strong stakeholder communication, technical coaching, design-review, and cross-time-zone collaboration skills.