Lead AI Solutions Engineer

The Mutual Group • New York, IL

3d • $150,000 - $180,000 • Hybrid

About The Position

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 role is a senior individual contributor position for an experienced engineer who can stay close to the work, solve complex technical problems, and help turn 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.

Requirements

• 8+ years of progressive technology experience across software engineering, integration engineering, automation, cloud, data, platform engineering, IT operations, or enterprise technology delivery.
• 5+ years of experience with AI, machine learning, automation, advanced analytics, intelligent platforms, developer productivity tools, or emerging technology capabilities.
• Strong hands-on experience with Generative AI patterns, including LLMs, SLMs, embeddings, prompt engineering, RAG, vector databases, semantic search, evaluation frameworks, and enterprise knowledge integration.
• Experience building Agentic AI solutions using agents, tool/function calling, orchestration, human-in-the-loop workflows, context management, guardrails, monitoring, and safe deployment practices.
• Familiarity with Model Context Protocol (MCP) or similar methods for connecting AI systems to enterprise tools, APIs, data sources, and workflows.
• Strong engineering fluency across APIs, microservices, cloud platforms, data integration, CI/CD, DevSecOps, test automation, observability, cybersecurity, identity, and privacy.
• Experience building production-grade applications, integrations, automation components, reusable engineering assets, or developer productivity tools.
• Experience working in regulated environments with strong security, privacy, auditability, operational readiness, and compliance expectations preferred.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related field required.
• Hands-on technical leader who leads from the front and stays close to the work.
• Practical problem solver with strong analytical reasoning and bias for action.
• Strong delivery owner who manages complexity, dependencies, quality, and risk.
• Collaborative partner who works effectively across engineering, infrastructure, data, security, operations, architecture, and business teams.
• Clear communicator who can explain technical options, tradeoffs, and risks.
• Continuous learner who raises engineering standards and helps teams adopt AI-enabled ways of working.

Responsibilities

• 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 AI capabilities with enterprise applications, data platforms, APIs, document repositories, workflow tools, service management platforms, and cloud services.
• Apply secure-by-design and privacy-by-design practices in solution development, including identity and access controls, sensitive data handling, logging, monitoring, and output validation.
• Partner with Security, Data, Architecture, and AI & Technology Risk Governance teams to ensure AI solutions meet enterprise expectations for responsible use, auditability, reliability, and compliance.
• Prepare solutions for production through testing, observability, support documentation, runbooks, deployment readiness, and operational handoff.
• Identify technical risks, dependencies, performance issues, cost considerations, and support needs early in the delivery process.
• Lead technical execution for priority AI initiatives, coordinating across engineers, architects, contractors, vendors, and partner teams.
• Mentor engineers on AI solution patterns, coding practices, integration approaches, testing, documentation, and production readiness.
• Create reusable technical documentation, implementation guides, reference examples, and enablement materials for broader IT adoption.
• Participate in design reviews, code reviews, architecture discussions, and technical problem-solving.
• Model a hands-on, accountable, continuous-learning mindset that raises the bar for engineering quality and delivery.

Benefits

• Competitive base salary plus incentive plans for eligible team members
• 401(K) retirement plan that includes a company match of up to 6% of your eligible salary
• Free basic life and AD&D, long-term disability and short-term disability insurance
• Medical, dental and vision plans to meet your unique healthcare needs
• Wellness incentives
• Generous time off program that includes personal, holiday and volunteer paid time off
• Flexible work schedules and hybrid/remote options for eligible positions
• Educational assistance

What This Job Offers

• Job Type: Full-time
• Career Level: Senior