28 Gpu Jobs - Page 2

Job Title: Windows Display Driver (Freelance) Job Type: Freelance About the Role: We are seeking a highly experienced Senior Windows Video Driver Consultant to support our development and debugging of custom video drivers for Windows-based platforms. You will work closely with internal engineering teams to ensure optimal performance, compatibility, and stability of video drivers across various hardware configurations. Key Responsibilities: Develop, debug, and optimize Windows video drivers (WDDM). Provide expert-level consultation on driver architecture and design improvements. Collaborate with hardware teams to ensure proper integration with video subsystems. Analyze logs, dumps, and performance metrics to troubleshoot complex issues. Assist in the certification process (WHQL) and ensure compliance with Microsoft driver standards. Support bring-up of new platforms or features (DirectX, HDR, multi-display, etc.). Create technical documentation and handover materials for internal teams. Required Skills & Qualifications: 4+ years of hands-on experience with Windows driver development. Deep knowledge of WDDM , DirectX , and Windows kernel-mode development. Strong experience with KMDF/UMDF , DXGI , and graphics stack debugging tools . Solid C/C++ programming and debugging skills in a Windows environment. Experience with tools such as WinDbg, GPUView, and ETW. Understanding of video pipeline, display protocols (HDMI, DisplayPort), and hardware interfaces. Experience working with OEMs or IHVs is a strong plus. Ability to work independently and communicate technical ideas effectively.

Software Architect Generative AI & LLM Systems job location Hyderabad - Noida or Gurgaon Job Overview We are seeking a highly experienced and hands-on Software Architect to lead the design and deployment of Large Language Model (LLM)-powered applications across cloud and on-prem environments. This role demands deep expertise in full-stack software development, high-performance inference systems, and cutting-edge generative AI workflows. You will play a key role in scaling AI infrastructure, maximizing throughput, and educating cross-functional teams on best practices for building LLM-driven solutions. Key Responsibilities LLM Deployment & Infrastructure Design: Architect, deploy, and maintain LLMs on cloud-based GPU clusters (e.g., AWS, GCP, Azure) or on-premise hardware including NVIDIA HGX and smaller GPU-accelerated instances. Bonus points for experience deploying containerized LLM applications in GPU clusters. Performance Optimization on Software Layer: Optimize LLM serving stacks using frameworks such as vLLM, TensorRT-LLM, or DeepSpeed to improve inference throughput and reduce time-to-first-token latency. Prompt Engineering & Optimization: Design, test, and refine prompts for LLMs to extract the highest quality output. Mentor team members on prompt engineering strategies and few-shot examples. I nference Efficiency & Scalability: Architect systems to maximize low-latency performance and time-to-first-token even under high demand. GenAI Application Architectu re: Build and lead GenAI application development using Langchain, designing modular pipelines for agents, tools, and memory systems. Define architectural patterns and reusable workflows. Team Enablement & Education: Educate and upskill engineering teams on best practices in GenAI development, inference performance, and prompt design through documentation, workshops, and code reviews. RAG with SQL-based Systems: Design and implement retrieval-augmented generation (RAG) pipelines that leverage SQL-like structured databases for high-relevance grounding. Vector Database Integration (Nice-to-Have): Bonus: Architect and optimize RAG systems using vector embeddings and specialized vector databases such as FAISS, Weaviate, or Pinecone. Requirements Must-Have Skills: 7+ years of full-stack development and software architecture experience Proven track record deploying LLMs in production, both on-premise and cloud GPU environments Strong hands-on experience with v LLM, Langchain, and model serving performance tuning Deep knowledge of prompt engineering, token economy, and optimizing LLM behavior Experience designing and scaling inference pipelines for latency and throughput Strong experience with Python and either TypeScript or Golan g Familiarity with deploying applications to hyperscalers (AWS, GCP, Azure) Strong knowledge of SQL databases and data retrieval strategies for grounding LLM responses Nice-to-Have Skills: Experience with vector databases and embedding-based retrieval in RAG pipelines Experience with orchestrating containerized LLM deployments using Kubernetes or Ray Familiarity with streaming inference systems and token-by-token UX optimizations Background in AI/ML systems, MLOps, or research-to-prod workflows conact 95134 87487

Develop and optimize HPC applications and algorithms using CUDA, MPI, OpenMP on Azure and cluster systems. Support scientific teams by modernizing codebases and enabling GPU acceleration. Required Candidate profile Software engineer with 5+ years in HPC programming, scientific code optimization, GPU computing, and collaboration with research teams.