Artificial Intelligence Optimization Specialist

3 weeks ago

Delhi, Delhi, India Pixis Full time
Unlock Your Potential in AI

About Pixis:
Pixis is a cutting-edge codeless technology company based in the US, revolutionizing the world of accessible AI. Our mission is to empower brands to scale their performance marketing efforts and enhance decision-making with seamless AI integration. Since its inception, we have been driven by a vision to develop powerful AI infrastructure that enables marketers worldwide to harness robust plug-and-play AI products without writing a single line of code.

We have secured a total funding of $209M across Series A, B, C, and C1, backed by renowned investors including SoftBank Vision Fund, Touring Capital, Grupo Carso, General Atlantic, Celesta Capital, and Chiratae Ventures. Our esteemed customer base comprises global brands such as DHL Express, Joe & The Juice, Claroshop, Allbirds, L'Oreal, HDFC Bank, Skoda, Swiggy, Clar, and SmartAsset, among others.

Our diverse team of over 300 professionals from 14 geographies is dedicated to crafting transformative AI products that help customers maximize their marketing and demand generation potential.

Discover the possibilities at https://pixis.ai/

Job Overview:

We are seeking an exceptional Artificial Intelligence Optimization Specialist with 3+ years of experience in optimizing deep learning model performance, particularly in computer vision and natural language processing domains. The ideal candidate will possess a strong background in software engineering, deep learning, and performance optimization techniques.

Responsibilities:

1. Inferencing and Performance Optimization: Optimize deep learning model inferencing runtime and quality for high-performance computing environments.
2. Cutting-Edge Model Integration: Collaborate with our AI research and development team to integrate the latest advancements in deep learning into our projects, focusing on computer vision tasks using models like Stable Diffusion, ControlNet, and LAMA.
3. Inference Framework Expertise: Utilize inference frameworks like TensorRT, ONNX, and ONNXRuntime to accelerate and streamline model deployments.
4. Fine-Tuning Strategies: Implement fine-tuning strategies to improve model performance on specific datasets or tasks.
5. Technical Guidance: Provide technical guidance on best practices for deep learning model optimization and deployment.
6. Performance Benchmarking: Conduct rigorous performance testing and benchmarking to ensure that models meet the required efficiency and accuracy standards.

Skills and Qualifications:

1. Deep Learning Expertise: Extensive experience with PyTorch and familiarity with TensorFlow. Understanding of deep learning architectures and ability to implement efficient models.
2. Inference Frameworks: Proficiency in using TensorRT, ONNX, and ONNXRuntime for model optimization and deployment.
3. Model Optimization: Strong knowledge of techniques for optimizing model inferencing runtime and quality, including quantization, pruning, and knowledge distillation.
4. Computer Vision Models: In-depth understanding of modern computer vision deep learning models like Stable Diffusion, ControlNet, and LAMA.
5. Software Engineering: Excellent programming skills in Python and experience in writing high-quality, maintainable code.
6. Performance Engineering: Expertise in profiling, benchmarking, and optimizing software and algorithms for maximum performance.
7. Integration: Understanding of Python API Frameworks such as FastAPI. Capability to build robust and fast synchronous or asynchronous APIs. Experience working with queuing systems and caching systems.
8. Fine-Tuning: Knowledge of strategies for fine-tuning deep learning models to improve performance on specific tasks or datasets.

Bonus Skills:

1. GPU Programming: Experience with CUDA and GPU programming for deep learning. Familiarity with deep learning deployment on cloud platforms (AWS, GCP, Azure). Understanding of container technologies like Docker and orchestration tools like Kubernetes.
2. DevOps and Cloud Expertise: Experience with version control systems, preferably Git. Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitHub Actions). Knowledge of cloud computing services and deployment (AWS services like EC2, S3, Lambda, etc.).

The ideal candidate will be a problem-solver who excels in a fast-paced environment and is passionate about pushing the boundaries of AI technology. If you are eager to work on state-of-the-art deep learning projects and have a proven track record of improving model performance, we would love to hear from you.

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