AI Engineer Intern

About the job

About the Role We are seeking a highly driven technical problem solver to support the end-to-end development of 0→1 AI applications powering high-value, business-critical initiatives. This internship sits at the intersection of AI applications, engineering, and customer problem-solving. You will contribute throughout the full lifecycle—from concept through prototype and deployment—working on architecture design, hands-on experimentation, cross-functional collaboration, and delivering measurable business impact. Key Responsibilities: • Collaborate with cross-functional teams (product, engineering, data, business) to solve high-impact AI problems with AI solutions. • Translate business ideas and complex problem statements into scalable AI-based technical solutions and prototypes. • Design, implement, and optimize prompt engineering strategies (prompt design, evaluation, templates, guardrails) to improve model accuracy, reliability, and user experience. • Contribute to the architecture and delivery of 0→1 AI applications, including experimentation, MVP development, deployment, and iterative improvements. • Communicate project insights, progress, and challenges to leadership as part of flagship AI initiatives. • Support hands-on development of model pipelines, integrations, and cloud-based infrastructure to enable AI functionality. • Help establish best practices and technical standards for building safe, reliable, and scalable AI solutions. Skills: • AI Product Development: Design and prototype AI-powered features from 0→1, building demos and POCs to validate feasibility while collaborating cross-functionally and communicating strategy to technical and non-technical stakeholders • Prompt Engineering & LLM Operations: Develop and optimize prompts with guardrails, implement safety measures and hallucination mitigation, and tune performance across LLM and RAG workflows • Retrieval & RAG Systems: Engineer retrieval pipelines using embeddings and vector databases, optimize context through chunking and metadata enrichment, and build hybrid rule-based + model-based systems • AI Systems Architecture: Design robust LLM/RAG pipelines with intelligent error recovery, human-in-the-loop workflows, and policy enforcement guardrails while optimizing for performance, latency, and cost • Backend Development: Build production-ready backend services and APIs using Python to expose AI functionality at scale • Model Evaluation & Quality Assurance: Develop comprehensive evaluation methodologies including automated and human assessments for safety, hallucination risk, and UX quality while monitoring for drift and degradation • AI Research & Innovation: Assess emerging foundation models, experiment with fine-tuning approaches, evaluate hosting infrastructure, and maintain expertise in evolving LLM best practices • Strategic Execution: Balance rapid experimentation with measured business impact, integrating user feedback iteratively while managing ambiguity in emerging AI capabilities Non-Negotiables • High ownership and accountability • Strong written and verbal communication skills • Comfort with ambiguity and experimentation • Bias toward action, measurable outcomes • Ability to educate stakeholders on AI value and constraints

Requirements

• AI Product Development
• Prompt Engineering
• Backend Development

Preferred Technologies

• AI Product Development
• Prompt Engineering
• Backend Development

Benefits

• Exposure to real-world enterprise AI challenges
• Hands-on experience shipping 0→1 products
• Mentorship from experienced AI engineers and product leaders