Ai / Ml Engineer

AI / ML Application Technical Analyst Ensuring emerging AI models meet the expected performance on target hardware. Ensuring easy to use and proficient Developer Experience for end users. Verifying that these models get deployed in an efficient and scalable way. Hands on proficiency in AI Software Stack, Machine Learning Techniques, Product Verification, Validation & Benchmarking. This is an exciting opportunity to work at the cutting edge of AI on both SW infrastructure and enablement on state-of-the-art AI hardware technologies! About the Ideal Candidate :

You’ll enjoy working in this team if you like digging into low-level system integrations, compiler support, quantizer and framework optimizations. At the intersection of software engineering and machine learning, you will be part of a team of AI / ML engineers verifying and analyzing implementation of latest AI / ML models from the popular repositories like Hugging Face in Python / C++ on state-of-the-art AI devices. The focus will be to ensure required model performance is achieved without sacrificing ease of use. Linux or embedded devices experience would be a great plus for the job. Experience :

Overall Experience : 5+ Years of experience in development / triaging / debugging roles AI / ML Knowledge : 2+ years’ experience with common AI / ML algorithms, architectures, tools, and practices. Familiarity with model training, inference, and quantization. AI / ML Frameworks : Knowledge about PyTorch, ONNX, TensorFlow and any other framework AI / ML Model Repositories : Familiarity with repositories like Huggingface, Ultralytics, FuxiCTR Technical Verification : Familiarity with software or model verification processes, ensuring that software / models meet specified requirements. Necessary Hard Skills :

Python and / or C / C++ programming Linux and shell script GitHub Dockers / Kubernetes / Containers Necessary Soft Skills :

Strong communication Strong analytical and problem-solving skills Good teamwork and ability to comprehend the big picture Quickly picking up details of a new domain Optional but Desirable Skills and Experience Understanding of installing Linux / Windows drivers and debugging failures of drivers Knowledge about regression testing / verification tools Debugging code to pin-point areas of problems Working with end users to address their questions, debugging their problems and documenting solutions. CI / CD tools and pipeline Experience with DevOps and CI / CD practices. Experience with data engineering and machine learning pipelines.