Associate Principal - Architecture

We are looking for an experienced Application Architect with deep, hands-on expertise in Generative AI to join our team as a GenAI Solution Architect focused on agentic engineering. The ideal candidate actively codes, has successfully deployed agentic and GenAI solutions in production environments, and excels at translating AI-driven engineering strategies into actionable delivery models for stakeholders across multiple time zones.

Key Responsibilities

GenAI Solution Architecture and SDLC Acceleration:
Define the target-state architecture and create adoption roadmaps for AI-led software development lifecycles, integrating AI agents into planning, coding, review, testing, and deployment phases. Expand agentic workflows by leveraging engineering design and specification-driven development, using design documents as authoritative sources and incorporating AI-assisted documentation and agent reviewers at key gates. Architect comprehensive GenAI solutions including agentic developer workflows, retrieval-augmented generation (RAG) pipelines, and large language model applications tailored for insurance workloads, scaling successful pilots to production. Lead AI-accelerated architecture transformations involving core and legacy modernization as well as event-driven architecture implementations. Apply AI agents for code comprehension, documentation, migration, test generation, and event schema scaffolding. Design AI-enabled business workflows with human-in-the-loop controls, ensuring auditability and exception management. Mentor offshore teams in agent-assisted engineering, establishing quality gates and guardrails for AI-generated code. Provide technical leadership to GenAI engineers by setting direction, reviewing designs, and building agentic engineering capabilities. Define and track acceleration metrics such as cycle time, throughput, and quality, continuously refining delivery blueprints. Enforce architecture standards, design patterns, responsible AI practices, and governance aligned with insurance data privacy, security, and regulatory requirements. Develop evaluation and guardrail mechanisms including golden datasets, hallucination checks, and production monitoring.

Agentic Engineering and Tooling:
Design multi-agent systems with defined roles such as orchestrator, coder, reviewer, and tester, supported by task routing, state management, and human-in-the-loop checkpoints. Configure and manage enterprise GenAI developer tools including coding agents, AI pair-programming solutions, custom commands, SDKs, and orchestration frameworks. Design and deploy Model Context Protocol (MCP) servers to securely expose customer tools, APIs, and data sources to AI agents using appropriate function-calling patterns. Select and integrate foundation models like Anthropic Claude, OpenAI GPT, Gemini, Llama, and Mistral through APIs or managed platforms such as AWS Bedrock, Azure OpenAI, Azure AI Foundry, and Google Vertex AI. Develop model-tiering and routing strategies balancing capability, data residency, cost, latency, and workload volume. Deploy and operate GenAI workloads in enterprise cloud environments with private endpoints, network isolation, identity and access management, quota controls, regional availability planning, and cost management mechanisms.

Context Engineering, Business Ontology, and Domain Knowledge:
Extract and curate customer knowledge from legacy code, documentation, wikis, and tickets, maintaining versioning and freshness to keep context accurate amid evolving systems and regulations. Prepare data for AI by profiling, cleansing, normalizing, deduplicating, correcting OCR noise, structuring unstructured information, and curating versioned datasets including synthetic data where appropriate. Protect data throughout AI pipelines via de-identification, PII masking, redaction, lineage tracking, and role-based access controls for prompts, retrieval corpora, evaluation datasets, and fine-tuning data. Implement metadata management and data governance practices such as business glossaries, data catalogs, lineage, and traceability for ontologies and datasets. Apply disciplined prompt and context engineering techniques, including few-shot examples and structured reasoning, to enhance solution reliability and consistency.

Required Qualifications
- Strong background in application architecture combined with recent, hands-on experience in GenAI engineering.
- Proven track record of deploying agentic or GenAI solutions in live enterprise environments.
- Ability to maintain hands-on coding while providing architectural leadership and technical governance.
- Experience translating AI strategies into practical delivery models for distributed teams.
- Working knowledge of multi-agent system design, retrieval-augmented generation, large language model applications, Model Context Protocol, cloud deployment, responsible AI, evaluation, monitoring, and data governance.
- Experience working within privacy-sensitive and regulated industries, with insurance domain expertise highly desirable.
- Excellent communication skills, technical coaching ability, design review experience, and capability to collaborate across multiple time zones.

Preferred Qualifications
- Familiarity with architecture governance and MLOps practices.
- Expertise in architecture patterns and styles, Generative AI/OpenAI, vector databases, Python programming, and RAGAS (Retrieval Augmented Generation Assessment) framework.

This role offers the opportunity to lead cutting-edge AI architecture initiatives in a regulated, enterprise environment, working with diverse teams to accelerate software development lifecycles through innovative agentic engineering solutions.