Senior AI Engineer

About the Role

The role is part of a small, embedded AI pod that partners with various teams within the university. Your primary focus will be on engineering, with a two-pronged approach: AI-augmented engineering and building AI-powered products. You will leverage AI tooling, coding agents, and modern dev workflows to ship high-quality solutions quickly and efficiently.

Reporting to AI Engineering leadership, you will set technical direction inside the pod and mentor engineers. You will also contribute to shaping AI Engineering practice across pods, ensuring that the team is always pushing the boundaries of what is possible.

What You Will Do

• Design and ship AI solutions, including agent orchestration, tool use, memory, retrieval, and integrations that connect them to SNHU systems of record and data
• Oversee architectural decisions and their documentation, making choices that the host team or sustainment owner can maintain after the pod departs
• Design and operate solution-specific tests and evaluations, building the instrumentation that makes offline and in-product signals actionable
• Implement agent and orchestration patterns using frameworks like LangGraph/LangChain, CrewAI, AutoGen, MCP, or custom approaches
• Surface delivery and technical risk early, making scope and architecture trade-offs decisively rather than letting them drift
• Use AI development tools like Claude Code, Cursor, Codex, GitHub Copilot, and latest tools as a daily part of how you ship
• Maintain a clear point of view on where these tools help, where they fail, and how to validate their output
• Pair with the host team's engineers on AI-augmented development practice, so they leave the engagement more capable, not just better served
• Produce the standard pod artifact set, which includes Agent Design Briefs, ADRs, Evaluation Scorecards, Deployment and Operations Runbooks, and retros
• Ensure the quality bar is high enough to allow the next pod and the next domain team to reuse what you built
• Contribute to shared AI Engineering assets that codify what works and increase capabilities across the team

What We Are Looking For

• 5+ years of experience in AI engineering, with a strong focus on machine learning, natural language processing, and software development
• Proficiency in programming languages like Python, Java, or C++, with experience in AI frameworks and libraries
• Strong understanding of AI concepts, including deep learning, computer vision, and reinforcement learning
• Experience with cloud-based platforms like AWS, Azure, or Google Cloud, with knowledge of containerization and orchestration tools
• Excellent problem-solving skills, with the ability to break down complex problems into manageable components
• Strong communication and collaboration skills, with experience working in cross-functional teams
• Bachelor's or master's degree in computer science, AI, or a related field
• Experience with Agile development methodologies and version control systems like Git

Nice to Have

• Experience with DevOps tools like Jenkins, Docker, or Kubernetes
• Knowledge of data engineering and data science concepts, with experience working with data pipelines and data warehouses
• Familiarity with SNHU's technology stack and systems
• Experience with AI ethics and fairness, with a strong understanding of bias and mitigation strategies

Benefits and Perks

• Competitive salary and benefits package
• Opportunity to work remotely and set your own schedule
• Access to cutting-edge AI tools and technologies
• Collaborative and dynamic work environment
• Professional development opportunities, with a focus on AI and machine learning
• Flexible paid time off and holidays
• Health and wellness programs, including mental health support and employee assistance
• Remote work stipend and home office setup support

• Tip: Make sure to highlight your experience with AI frameworks and libraries, such as TensorFlow or PyTorch, and provide specific examples of how you have applied these technologies in previous roles.
• Tip: Showcase your ability to work collaboratively in a remote environment, and demonstrate your strong communication and problem-solving skills.
• Tip: Be prepared to discuss your experience with cloud-based platforms and containerization tools, and explain how you have used these technologies to deploy and manage AI models.
• Tip: Emphasize your understanding of AI ethics and fairness, and provide examples of how you have addressed bias and mitigation strategies in your previous work.
• Tip: Consider including a portfolio or samples of your work, such as GitHub repositories or research papers, to demonstrate your technical expertise and passion for AI engineering.
• Tip: Be prepared to ask questions during the interview process, such as what the typical career path is for an AI engineer at SNHU, or what opportunities there are for professional development and growth.