AI Engineer - Campus and Graduate LevelExperience: 1-3 years of relevant experience through internships, academic projects, research, freelance work, or professional roles
Location: [Location / Hybrid / Remote]
Reports to: Agentic AI Architect / Senior Forward Deployed AI Engineer
Role OverviewNewRocket is seeking a hands-on
AI Engineer - Campus and Graduate Level to support the design, development, testing, and deployment of AI-enabled solutions for enterprise clients.
This role is ideal for an early-career engineer with foundational experience in software development, data, and generative AI who is eager to work on practical, high-impact applications of AI-including Claude-powered applications, agentic workflows, intelligent automations, retrieval-augmented generation (RAG), and ServiceNow-based solutions.
You will work closely with AI engineers, product managers, data scientists, platform teams, and client-facing consultants to prototype and deliver scalable AI capabilities. You will gain exposure to the full AI solution lifecycle, from use-case discovery and proof-of-concept development through production rollout, evaluation, monitoring, and continuous improvement.
Travel to clients and conferences as needed.
Key ResponsibilitiesAI Solution Development- Assist in building, testing, maintaining, and documenting AI-enabled applications, workflows, and reusable accelerators.
- Develop prototypes and proof-of-concepts using generative AI, large language models (LLMs), including Claude and other model providers as appropriate, RAG, APIs, and workflow automation tools.
- Support the development of agentic AI solutions that can use enterprise context, call approved tools or APIs, and help orchestrate enterprise processes.
- Assist with prompt and context engineering, including instruction design, examples, structured inputs, response formatting, and output constraints.
- Help build AI applications using structured outputs, tool use/function calling, human-in-the-loop review, and workflow orchestration patterns.
- Help integrate AI capabilities with enterprise platforms, including ServiceNow, where applicable.
- Write clean, maintainable, secure, and well-documented code following engineering best practices.
Retrieval, Data, Evaluation & Quality- Support data preparation, document ingestion, chunking, embedding, retrieval, prompt development, evaluation, and testing activities for AI solutions.
- Assist with RAG solutions that ground AI responses in authorized enterprise knowledge sources and data.
- Help assess model and workflow performance across accuracy, relevance, groundedness, reliability, latency, cost, safety, and user experience.
- Assist with the development of test cases, evaluation datasets, monitoring approaches, and feedback loops for AI applications.
- Support techniques that improve reliability and user trust, such as output validation, citation or source-grounding patterns, fallback handling, confidence thresholds, and escalation workflows.
- Identify issues, document findings, and contribute to iterative improvements across AI prototypes and production solutions.
Responsible AI & Security- Apply responsible AI principles in the development and testing of AI solutions, including awareness of model limitations, hallucinations, bias, privacy, and appropriate human oversight.
- Help implement safeguards for sensitive data, access controls, authorized data use, prompt-injection risks, and unsafe or unintended tool execution.
- Support human-in-the-loop workflows for sensitive, high-impact, low-confidence, or exception-based AI decisions.
- Document solution behavior, known limitations, test results, and operational considerations for internal teams and client stakeholders.
Cross-Functional Collaboration- Collaborate with product, engineering, design, data science, ServiceNow platform, and consulting teams to translate business needs into technical solutions.
- Participate in requirements gathering, solution-design sessions, sprint planning, code reviews, and retrospectives.
- Support client-facing teams with technical research, demos, proof-of-concept development, and implementation activities.
- Communicate technical concepts, solution behavior, and findings clearly to both technical and non-technical stakeholders.
- Contribute to internal documentation, technical playbooks, reusable components, prompt libraries, evaluation assets, and knowledge-sharing sessions.
AI Innovation & Research- Stay current on emerging AI technologies, frameworks, Anthropic and Claude capabilities, and enterprise use cases.
- Complete relevant Anthropic training, partner enablement, and technical education opportunities as available through NewRocket's partnership.
- Research opportunities to apply generative and agentic AI to improve operational efficiency, employee experiences, customer service, knowledge management, and enterprise workflows.
- Contribute ideas for new AI capabilities, reusable intellectual property, Agent Packs, accelerators, and product enhancements.
Required Qualifications- Currently pursuing or recently completed a degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or a related technical discipline.
- 1-3 years of relevant experience through internships, co-ops, research, freelance work, academic projects, or professional roles.
- Experience with one or more programming languages, preferably Python, JavaScript/TypeScript, Java, or similar languages.
- Foundational understanding of software engineering concepts, APIs, databases, source control, and cloud-based applications.
- Exposure to generative AI, LLMs, prompt engineering, machine learning, data science, or automation concepts.
- Familiarity with LLM application concepts such as context windows, token usage, embeddings, vector search, RAG, tool use/function calling, structured outputs, and model evaluation.
- Familiarity with tools or frameworks such as Git, REST APIs, SQL, Docker, LLM APIs, LangChain, LangGraph, LlamaIndex, or cloud AI services.
- Foundational understanding of responsible AI concepts, including data privacy, model limitations, human oversight, and safe AI deployment.
- Strong problem-solving skills, curiosity, attention to detail, and willingness to learn in a fast-paced environment.
- Ability to communicate technical concepts clearly to both technical and non-technical stakeholders.
Preferred Qualifications- Experience building AI, automation, chatbot, workflow-based, or data-driven projects through coursework, research, internships, hackathons, or personal projects.
- Exposure to Claude, the Anthropic API, Anthropic Console, Anthropic Academy learning, or Claude-focused implementation guidance.
- Experience using LLM APIs to build conversational AI, document-processing, summarization, search, knowledge-assistant, or workflow-automation applications.
- Understanding of RAG, vector databases, embeddings, semantic retrieval, AI agents, tool use, or human-in-the-loop workflow design.
- Familiarity with ServiceNow, including platform development, workflow automation, Virtual Agent, Predictive Intelligence, Now Assist, AI Agents, IntegrationHub, or Flow Designer.
- Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Familiarity with Model Context Protocol (MCP) concepts, secure API integration patterns, or connecting AI applications to enterprise tools and data sources.
- Experience working in an agile software-development environment.
- Interest in enterprise consulting, digital transformation, and applying AI to real-world business challenges.
What You Will Gain- Hands-on experience building enterprise-grade generative and agentic AI solutions, including Claude-powered applications where appropriate.
- Exposure to Anthropic-aligned practices for prompt and context engineering, RAG, tool use, structured outputs, model evaluation, and responsible AI.
- Experience across the end-to-end lifecycle of AI product and solution development-from discovery and prototyping to deployment, monitoring, and continuous improvement.
- Mentorship from experienced AI, engineering, product, ServiceNow, and consulting professionals.
- Opportunities to contribute to client-facing proofs-of-concept, reusable AI accelerators, Agent Packs, and production implementations.
- A strong foundation for a career in AI engineering, software development, data science, enterprise technology consulting, or AI product development.
