Senior AI Solutions Architect

About Magellan Jets

Founded in 2008 and now one of the largest private aviation providers, Quincy, MA-based Magellan Jets is a premier provider of private aviation solutions, offering a comprehensive range of Jet Card ownership, On-Demand Charter, and Aircraft Sales and Management. With a focus on personalized service, Magellan Jets is dedicated to delivering customized and unparalleled flying experiences to its Private Clients worldwide.

Magellan Jets and its FAA-certificated Flight Operations team lead one of the industry’s most comprehensive safety management systems and protocols providing peace of mind to the most discerning travelers.

The AI Solutions Architect is responsible for leading the design, development, and implementation of advanced AI and Generative AI solutions that drive strategic business impact. This role partners closely with cross-functional stakeholders to identify high-value opportunities, define AI strategies, and deliver scalable prototypes, POCs, and production-grade systems.

With deep expertise in software engineering, LLM technologies, and cloud architectures, the AI Solutions Architect builds and operationalizes AI frameworks, ensures technical excellence, and guides teams in adopting emerging AI/ML best practices. This position requires strong leadership, communication, and the ability to translate complex concepts into actionable solutions while navigating ambiguity in a rapidly evolving technological landscape.

• Lead the end-to-end design and execution of AI-powered POCs, prototypes, and solutions to validate strategic use cases with measurable outcomes.
• Collaborate with Sales, Marketing, Engineering, and Business teams to assess needs, define AI roadmaps, and identify high-impact opportunities for innovation.
• Evaluate emerging AI/ML/GenAI techniques and incorporate relevant technologies into solution architectures and organizational standards.
• Build robust backend frameworks, APIs, microservices, and cloud architectures to support scalable AI workloads and integrations.
• Develop data processing routines, training pipelines, and evaluation workflows using LLMs (e.g., GPT, Claude, LLaMA) and other ML techniques.
• Implement Retrieval-Augmented Generation (RAG), fine-tuning, prompt orchestration, and model monitoring to enhance performance and reliability.
• Monitor solution performance using quantitative and qualitative benchmarks, including hallucination detection, latency analysis, and prompt quality evaluation.
• Establish engineering best practices and leverage AI-assisted development tools (e.g., Git Hub Copilot) to improve productivity and code quality.
• Provide architectural guidance, technical mentorship, and leadership across cross-functional teams.
• Ensure backend systems are optimized for high transaction volumes and integrate seamlessly with existing platforms.
• Deploy AI solutions using cloud platforms (Azure or AWS), containerization technologies (Docker/Kubernetes), and serverless architectures.
• Implement CI/CD pipelines, manage version control, and ensure reliable, repeatable deployment processes.
• Drive adoption of agile/scrum methodologies to deliver iterative, high-quality solutions.
• Communicate complex AI concepts and technical decisions clearly to non-technical audiences.
• Build alignment across teams, influence technical direction, and ensure AI initiatives meet business objectives.
• Navigate ambiguity, establish clarity in evolving problem spaces, and lead the successful delivery of multifaceted initiatives.

• 6+ years of professional software engineering experience, or equivalent depth of experience demonstrating senior-level architectural ownership.
• 2+ years of hands-on experience building and deploying LLM-powered / GenAI applications in production.
• Experience developing and deploying ML models and integrating GenAI into production systems.
• Proven experience fine-tuning models, building pipelines, and evaluating model performance.

• AI/ML concepts, including LLM architectures, RAG patterns, fine-tuning methods, model evaluation, and prompt engineering theory.
• Software engineering principles, Java ecosystem…