AI Software Development Intern

Description

Job Details:

Join Intel's Datacenter and AI Software Ecosystem Engineering team to help accelerate the deployment and optimization of Generative AI solutions. This internship focuses on evaluating, benchmarking, and optimizing large language model (LLM) inference workloads across Intel platforms using state-of-the-art serving frameworks such as vLLM, SGLang, and OpenVINO. You will work on AI infrastructure, performance analysis, and cloud-native deployment solutions spanning on-premises and public cloud environments.

Project Context

Generative AI is rapidly transforming enterprise applications, with organizations increasingly deploying Large Language Models (LLMs) for conversational AI, knowledge retrieval, software development, content generation, and intelligent automation. As model sizes and user adoption continue to grow, delivering low-latency, cost-efficient, and scalable AI inference becomes a critical challenge.

Modern AI serving stacks combine model serving frameworks, optimized inference engines, cloud infrastructure, and observability platforms to deliver production-ready AI services. Organizations must balance performance metrics such as throughput, Time to First Token (TTFT), Time per Output Token (TPOT), latency, scalability, and total cost of ownership (TCO) while supporting a diverse range of deployment environments spanning on-premises datacenters, private clouds, and public cloud platforms.

This internship focuses on developing and validating modern AI inference solutions using technologies such as vLLM, SGLang, and OpenVINO. The work will involve deploying and benchmarking state-of-the-art LLMs, analyzing inference performance across Intel platforms, automating testing and deployment workflows, and evaluating cloud-native AI architectures. The objective is to help build scalable, efficient, and production-ready AI serving solutions that enable customers to maximize the value of Generative AI deployments.

What You'll Work On

You will explore the design, deployment, and optimization of Generative AI infrastructure, including:

LLM deployment and serving: Deploy and evaluate large language models using frameworks such as vLLM, SGLang, and OpenVINO

Performance analysis: Measure and optimize serving metrics including throughput, latency, TTFT, TPOT, concurrency, and resource utilization

AI benchmarking: Develop automated benchmarking workflows to characterize model performance across different hardware and software configurations

Cloud infrastructure: Deploy and evaluate AI workloads in AWS and Google Cloud Platform environments

DevOps and automation: Build CI/CD pipelines, test automation, and infrastructure tooling using Jenkins, Python, and Linux-based environments

Data management: Design and maintain systems for storing, querying, and analyzing benchmarking and telemetry data using PostgreSQL and related technologies

Solution validation: Investigate trade-offs between performance, scalability, accuracy, and cost across various deployment scenarios

Why This Internship?

Cutting-edge AI engineering: Work on real-world Generative AI deployment and inference optimization challenges

Industry impact: Contribute to technologies that help customers deploy and scale AI solutions on Intel platforms

Hands-on experience: Gain practical expertise with LLM serving frameworks, cloud infrastructure and execution

Mentorship: Learn from experts in AI systems, performance engineering, cloud computing, and software development

Broad technical exposure: Develop skills across AI, Linux, DevOps, cloud computing, databases, and infrastructure automation

Intel ecosystem: Build deep knowledge of AI deployment and optimization on Intel Xeon processors and Intel AI software solutions

Requirements

• Required Qualifications:
• Strong background in Artificial Intelligence, Machine Learning, Computer Science, or related field
• Programming skills: Proficiency in Python and experience developing software applications and automation tools
• LLM knowledge: Understanding of transformer-based generative AI models, inference workflows, tokenization, quantization, and serving architectures
• Linux proficiency: Experience working in Linux environments, shell scripting, system monitoring, and troubleshooting
• Academic standing: Currently enrolled in a Bachelor's, Master's, or PhD program in Computer Science, Artificial Intelligence, Software Engineering or related field
• Preferred Qualifications:
• Experience with one of LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or similar
• Experience benchmarking and performance tuning AI inference workloads
• Cloud fundamentals: Experience with AWS and/or Google Cloud Platform services
• DevOps knowledge: Understanding of CI/CD concepts and tools such as Jenkins, Git, and containerized workflows
• Knowledge of model optimization techniques including quantization, pruning, KV-cache optimization, and speculative decoding
• Experience working with container orchestration technologies such as Kubernetes and Docker
• Experience building APIs and services using Python frameworks such as FastAPI
• Personal or academic projects involving LLM deployment, retrieval-augmented generation (RAG), AI agents, or inference optimization
• Take the next step in your career by joining a team passionate about advancing the future of AI software. This internship offers a unique opportunity to develop your skills, contribute to impactful projects, and grow alongside industry-leading experts.
• Job Type:
• Student / Intern
• Shift:
• Shift 1 (Poland)
• Primary Location:
• Poland, Gdansk
• Additional Locations:
• Posting Statement:
• Position of Trust
• N/A
• EU Salary
• We offer a total compensation package that ranks among the best in the industry.
• The minimum Annual Salary for this job which could be performed in Poland is: 68 880,00 zł&xa;&xa;The job posting reflects the minimum base compensation for this role. Our standard internship rates are aligned with the specific job role and degree level. As part of our total compensation approach, the final offer may also include additional pay components and benefits. Your recruiting team will provide the confirmed base salary along with any applicable total compensation elements upon employment offer delivery.
• Work Model for this Role
• This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. Job posting details (such as work model, location or time type) are subject to change.