Weekend Remote Ai Engineer Jobs (NOW HIRING)

Southern New Hampshire University (SNHU) is hiring an AI Engineer to join the AI Engineering team. Reporting to AI Engineering leadership, you'll help build and operate the AI products. These products move SNHU's most important learning and operational problems forward. You'll work from inside small, embedded AI pods that collaborate with the SNHU teams who own those problems. You'll grow into senior ownership over time.

You'll work under technical direction from a Senior AI Engineer on the pod. You'll contribute alongside the AI Analyst and Domain Product Owner. As the program scales, you can expect to rotate across pods, which will allow patterns and practice to spread with you. Your seat is two-pronged:

You will work 100% remotely. #LI-Remote

• AI-augmented engineering: you use AI tooling, coding agents, and modern dev workflows daily, are improving how you use them, and bring that practice into pod work.

• Building AI-powered products: you implement AI solutions: agent and orchestration components, retrieval, evaluation, and the integrations they sit inside.

What You'll Do:

• Build agent orchestration, tool use, memory, retrieval, and the integrations that connect AI solutions to SNHU systems of record and data, under the technical direction of the pod's Senior AI Engineer.

• Participate in architecture, prompt design, and evaluation discussions - bring your perspective, contribute to Architectural Decision Records, and grow into ownership over time.

• Implement and operate solution-specific tests and evaluations; help maintain the instrumentation that makes offline and in-product signals actionable.

• Work hands-on with agent and orchestration patterns and frameworks (LangGraph/LangChain, CrewAI, AutoGen, MCP, or similar), building judgment about which fit what kind of problem.

• Surface delivery and technical risk early - flag blockers and ambiguities to the pod before they become problems. Write clean, well-tested code the host team or sustainment owner can maintain after the pod departs.

• Use AI development tools (Claude Code, Cursor, Codex, GitHub Copilot, and latest tools) daily; keep evolving how you work with them and share what's working with the team.

• Pair with the host team's engineers on AI-augmented development practice as the engagement allows, helping them leave the engagement more capable, not just better served.

• Contribute to the standard pod artifact set, which includes Agent Design Briefs, ADRs, Evaluation Scorecards, Deployment and Operations Runbooks, and retros. Ensure that your contributions meet a quality bar that lets the next pod and the next domain team reuse what you built.

• Lift what generalizes into shared AI Engineering assets - prompt and skill libraries, evaluation templates, reference architectures, practice handbook entries.

• entries.

Pod collaboration The canonical pod has three peers with joint accountability:

• AI Analyst - owns initiative framing, requirements craft, solution assessment, and lightweight delivery facilitation. You work with them to understand the requirements and user needs behind each technical task and communicate technical constraints and trade-offs back.

• Domain Product Owner - seated from the host team; carry domain knowledge and owns outcomes on the host side. You learn enough of their domain to make sound engineering trade-offs without always pinging others.

• Senior AI Engineer - sets pod-side technical direction and quality bar. You partner with them on architecture, scope, and quality, and grow into senior ownership over time.

• You will participate in discovery and shaping conversations from the start. You contribute to sprint planning, demos, and retros at the pace the pod sets. You bring questions and findings back to the pod as you build shared understanding of the business context.

• Partner with SNHU AI on governance, safety, evaluation, and adoption as they relate to your build work; follow the standards set at the pod and program level.

• Coordinate with IT on infrastructure, IAM, security review, and DevOps as pod work requires.

• Communicate progress, blockers, and resolutions to pod members and, where relevant, to host team partners.

What we're looking for:

• 3+ years building and shipping production software at scale in one or more modern languages. We hire for engineering aptitude and real shipped work, not a specific stack. We use Python as our working language; we don't require deep prior Python experience, but you'll need to make yourself productive in it quickly.

• Production experience in at least one major cloud (AWS, Azure, or GCP) - serverless compute, object storage, NoSQL.

• Hands-on work with LLMs - orchestration, prompts, basic evaluation - in production or through substantial hands-on exploration.

• Curiosity about and active learning in agentic frameworks (LangGraph/LangChain, CrewAI, AutoGen, and MCP).

• Working understanding of LLM evaluation - running evals, interpreting results, and developing a sense of where outputs need checking.

• Comfort with cloud-native development workflows - CI/CD and infrastructure-as-code concepts; able to get things running, not a specialist.

• Experience with a relational database such as MySQL, PostgreSQL, or Oracle.

• Active daily use of AI development tools, such as Claude Code, Cursor, Codex, and GitHub Copilot, is necessary . These tools have sped up your work in concrete ways, helping in certain areas while needing checking in others. Furthermore, it is crucial to provide evidence that you are enhancing how you use them.

• Experience working in teams or Agile delivery settings, including with non-engineering collaborators.