About the Team
The AI Solutions and Toolkit Engineering team is part of Intel's Data Center organization, focused on enabling modern AI workloads and solutions on Intel platforms.
We work across the AI ecosystem, collaborating with AI framework and runtime developers, performance and optimization teams, hardware architects, open-source communities, and solution engineering teams.
Our goal is to take deep platform capabilities and performance insights and translate them into practical, optimized AI solutions that developers and customers can deploy.
The team works across areas including LLM inference, Agentic AI, AI frameworks and runtimes, workload optimization, open-source software, and datacenter AI systems.
Key Responsibilities
Characterize and optimize LLM and Agentic AI workloads across the complete software and hardware stack.
Identify performance bottlenecks across AI frameworks, inference runtimes, operating systems, memory, compute, and platform architecture.
Design and develop hardware-aware and hardware-agnostic optimizations for AI frameworks and runtimes.
Optimize LLM inference techniques including KV-cache management, attention, continuous batching, and quantization.
Work hands-on with serving stacks such as vLLM, SGLang, TGI, or equivalent technologies.
Analyze host and accelerator performance using tools such as VTune, EMON/PMU, perf, top-down analysis, and roofline modeling.
Debug complex performance issues spanning Python frameworks, native code, Linux, runtime libraries, and hardware.
Convert performance findings into software changes, upstream contributions, BKCs, and reusable optimization guidance.
Collaborate with open-source communities to upstream and integrate performance improvements.
Translate silicon-level findings into actionable recommendations for framework developers, while bringing application and framework insights back to Intel platform and silicon teams.
Qualifications
Minimum Qualifications
BE +10 years / M.Tech +8 years in Computer Science, Computer Engineering, Electrical Engineering, or a related discipline.
Significant hands-on experience in performance engineering, systems optimization, or workload characterization on server-class platforms.
Demonstrated ability to identify complex performance bottlenecks and deliver measurable performance improvements.
Strong programming skills in Python and C++, or equivalent systems programming experience in C/Rust.
Strong understanding of server architecture including cache hierarchy, memory bandwidth, NUMA, interconnects, and system-level performance behavior.
Hands-on experience with Linux performance and system technologies such as affinity, huge pages, cgroups, eBPF, ftrace, perf, or equivalent tools.
Preferred Qualifications
Deep understanding of LLM inference, including KV-cache, paged attention, continuous batching and quantization such as INT8, INT4, or FP8.
Hands-on experience modifying or optimizing AI serving stacks such as vLLM, SGLang, TGI, OpenVINO, or equivalent.
Experience building, profiling, or optimizing Agentic AI systems using technologies such as LangGraph, CrewAI, AutoGen, MCP-based frameworks, or similar platforms.
Experience with hardware counter-level performance analysis using VTune, EMON/PMU, perf, top-down analysis, or roofline modeling.
Contributions to significant open-source projects, including upstream code contributions.
Ability to communicate across abstraction layers, translating silicon-level findings for software developers and application-level requirements for hardware architects.
Why Join This Team?
This role gives you the opportunity to work across boundaries that few engineering positions span:
Agentic AI & LLMs → AI frameworks → inference runtimes Linux → server architecture → silicon
You won't be limited to measuring performance or optimizing an isolated component. You'll help connect the entire stack, identify where the real bottlenecks are, implement improvements, and make those optimizations available to developers and customers.
The result is tangible: better AI performance today, stronger software solutions for customers, and insights that can influence tomorrow's Intel platforms.
Job Type
Experienced Hire
Shift
Shift 1 (India)
Primary Location
India, Bangalore
Additional Locations
Posting Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
- 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.
ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
