Accelerated Computing Engineer

Job Title: Accelerated Computing Engineer Entry Level

Experience Level: 02 Years

Location: Vellore

Employment Type: Full-time

About the Role

We seek a driven Accelerated Computing Engineer to join our innovative team in Vellore. This entry-level role offers a unique opportunity to work with advanced AI/ML models, accelerated computing technologies, and cloud infrastructure while collaborating on cutting-edge research and deployment projects.

You will work with a variety of state-of-the-art models such as BGE-Large, Mixtral, Gemma, LLaMA, and Stable Diffusion, as well as other fine-tuned architectures, to solve real-world computing challenges through advanced AI/ML infrastructure solutions.

Key Responsibilities

• Customer Interaction & Analysis: Work closely with customers to analyze technical and business needs, translating them into robust, AI-driven solutions.
• Model Deployment & Optimization: Develop and deploy advanced AI/ML models such as LLaMA, Mixtral, Gemma, and other GenAI models while optimizing their performance for varied computing environments.
• Performance Testing & System Benchmarking: Execute advanced test scenarios and performance benchmarks across AI/ML models and distributed systems to ensure optimal performance.
• Infrastructure & Model Research: Research, configure, and maintain infrastructure solutions (using tools like TensorRT and PyTorch) supporting our models and accelerated computing workloads.
• AI/ML Model Integration: Support and deploy models such as Stable Diffusion, BGE, Mistral, and custom fine-tuned models into end-to-end pipelines for AI/ML-driven solutions.
• Automation & Process Improvements: Drive automation strategies to streamline workflows, improve testing accuracy, and optimize system performance.
• Technical Liaison: Served as the technical bridge by collaborating with product development teams, tracking customer feedback, and ensuring timely resolutions.
• Model Configuration & Troubleshooting: Create custom scripts, troubleshoot advanced configurations, and support tuning efforts for AI/ML model customization.

Skills & Qualifications

Required Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical discipline.
• Strong foundational knowledge of AI/ML model deployment and cloud infrastructure.
• Proficiency with AI/ML frameworks & libraries, including PyTorch, TensorRT, and Triton.
• Hands-on experience with deployment models such as LLaMA, Mixtral, Gemma, and Stable Diffusion.
• Familiarity with distributed computing environments and orchestration tools like Kubernetes.
• Proficiency in workflow automation, performance tuning, and large-scale system debugging.
• Understanding of cloud computing technologies and infrastructure architecture, including storage, networking, and computing paradigms.

Preferred Skills:

• Experience working with object storage technologies like AWS S3, Azure Blob Storage, and MinIO.
• Familiarity with advanced AI/ML model frameworks such as Gemma-2b, Mixtral-8x7b, Mistral-7b-instruct, and other fine-tuned AI models.
• Expertise in GPU configuration and tuning for AI/ML workloads, including drivers and machine learning optimization strategies.
• Familiarity with serverless computing and Function as a Service (FaaS) concepts.
• Experience with infrastructure as code (IaC) and performance benchmarking methodologies.