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AI Research Engineer

At Fractal Analytics, we are leveraging cutting-edge deep-learning and machine-learning based solutions to address various business problems for leading Fortune 500 companies. We are looking to hire Deep-learning / Machine-learning AI Research engineers to help build scalable DL/ML enabled systems. 

As an AI Research Engineer, you will be responsible for building deep learning based scalable AI infrastructure to productionize deep learning models and their end-to-end lifecycle.

Your responsibilities

·       Ability to understand client requirements, design and drive the solutions. 

·       Basic understanding of Generative AI models, Transformers, object detection, semantic and instance segmentation, key point detection and object tracking algorithms etc.

·       Basic understanding of GANs, self-supervised, zero shot, few shot learning techniques. 

·       Excellent with Linear Algebra, Statistics and probability theory. 

·       High proficiency in Python programming knowledge. (Must)

·       Experience with usage of frameworks like Pytorch, Tensorflow, Scikit-learn etc. 

·       Experience working with distributed computing.  

·       Ability to evaluate the latest research developments in productionizing and optimizing deep learning models and help build state of the art capabilities in these areas within Fractal. 

·       Ability to leverage cloud and experiment at scale (Azure / AWS / GCP) 

·       Experience in creating APIs for deep learning solutions using FastAPI or Flask or Django

·       Experience in containerizing and orchestrating solutions using docker, docker-compose, Kubernetes etc.

·       Good understanding of micro-services, REST API framework, gRPC protocols etc.

·       Experience working on source control – git, CI-CD pipelines using Jenkins/gitlab CI-CD

·       Basic knowledge on working with remote servers and setting up development infrastructure wrt IDEs

Good to have: 

·       Deep understanding around building distributed and scalable infrastructure using Kubernetes – Deployments, Stateful set, Service, Persistence Volume/Claims etc

·       Experience on working with deep learning model optimization techniques – model pruning, sparsification, int-4/int-8 quantization, fp-16/bf-16 precision model conversion etc using ONNX, TensorRT and other supported tools.

·       Understanding the effect of different deployment strategies on network latency, throughput and ability to design a system as per SLAs.

We expect you will have: 

·       Technology savvy, updated on the latest research in DL, adaptable, so you can develop new solutions that match the evolving nature AI solutions. 

·       Personal drive and intellectual curiosity to do what hasn’t been done before, coupled with an appreciation for overcoming challenges. 

·       Good communication skills 

·       A Bachelor’s/Master’s degree in computer science or a related field. 

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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