Note: The job is a remote job and is open to candidates in USA. The Mutual Group is seeking a Lead AI Solutions Engineer to spearhead the design and delivery of AI-enabled solutions that align with their AI-First IT agenda. This role requires an experienced engineer to translate complex business needs into practical solutions while collaborating with various teams to ensure quality and scalability.
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
Skills
- 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
- 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
- Experience working in regulated environments with strong security, privacy, auditability, operational readiness, and compliance expectations
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
Company Overview