Lead AI Solutions Engineer

Job Description

The Lead AI Solutions Engineer will lead hands‑on design and delivery of AI-enabled solutions, prototypes, accelerators, integrations, and reference implementations that support The Mutual Group’s and its member insurance carriers AI‑First IT agenda. This senior individual contributor position requires an experienced engineer capable of staying close to the work, solving complex technical problems, and turning AI concepts into secure, practical, production‑ready capabilities.

This role will focus heavily on applying AI to software engineering, automation, developer productivity, IT operations, and repeatable delivery patterns. The Lead AI Solutions Engineer will also support AI‑enabled business use cases and help build reusable components for future AI platforms. The successful candidate will be comfortable moving between rapid experimentation and disciplined engineering, with a strong focus on security, quality, scalability, and measurable business value.

The role will work closely with AI platform leaders, architects, application teams, infrastructure and operations teams, data teams, security, risk governance, and business partners. This individual will help prove what works, document patterns, and enable broader adoption across IT over time.

Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in‑office schedule. The initial training period may require additional in‑office days.

• Lead hands‑on design, development, testing, and implementation of AI‑enabled solutions, prototypes, accelerators, and reference implementations.
• Translate business and technology needs into practical technical designs, working software, integration patterns, and reusable engineering assets.
• Build AI‑enabled capabilities for workflow automation, knowledge retrieval, document intelligence, summarization, classification, triage, decision support, engineering productivity, and operational efficiency.
• Partner with architects and senior engineers to ensure solutions are scalable, secure, observable, maintainable, and aligned with enterprise standards.
• Move quickly from proof of concept to production‑ready implementation while maintaining strong quality, documentation, and operational discipline.

• Develop generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, retrieval‑augmented generation, vector databases, semantic search, and enterprise knowledge integration.
• Build and refine agentic AI patterns, including tool and function calling, workflow orchestration, human‑in‑the‑loop controls, context management, guardrails, monitoring, and safe execution.
• Use Model Context Protocol (MCP) or similar approaches to connect AI systems with enterprise tools, APIs, data sources, and workflow actions in a secure and governed manner.
• Implement evaluation approaches for AI outputs, including accuracy, relevance, consistency, cost, latency, user feedback, and business value.
• Work with AI platform and architecture teams to contribute reusable patterns for model access, prompt handling, response validation, observability, and production support.

• Lead practical implementation of AI‑assisted engineering workflows, including coding, testing, documentation, requirements analysis, code review, quality engineering, and developer productivity.
• Build automation components that improve software delivery, release readiness, test generation, documentation quality, and engineering efficiency.
• Partner with Infrastructure and IT Operations teams to apply AI to observability, incident summarization, root cause analysis, predictive monitoring, runbook automation, service management, and operational productivity.
• Create reusable scripts, integrations, templates, workflows, and playbooks that help existing IT teams adopt AI‑enabled practices.
• Measure and communicate improvements in cycle time, quality, test coverage, automation adoption, operational efficiency, and service resilience.

• Integrate…