Principal AI Engineer

Position: Principal AI Engineer

Experience: 8+years

Your Role

Play an instrumental and influential role in driving Generative AI vision, strategy, and architecture.

Architect, build, maintain, and improve new and existing suite of GenAI applications and their underlying systems.

Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA.

Establish reusable frameworks to streamline model building, deployment and monitoring. Incorporate comprehensive monitoring, logging, tracing, and alerting mechanisms.

Build guardrails, compliance rules and oversight workflows into the GenAI application platform, such as establishing approval chains for model updates and staged rollout for production releases

Develop templates, guides and sandbox environments for easy onboarding of new contributors and experimentation with new techniques

Ensure development of user-facing applications in the GenAI application platform is easy and safe by enforcing rigorous validation testing before publishing user-generated models and implement a clear peer review process of applications

Contribute to and promote good software engineering practices across the team.

Your expertise

A master's degree or Ph.D in Computer Science, Artificial Intelligence, Machine Learning or a related field Proven experience in leading AI projects from conception to deployment in a consultancy or start-up environment

Extensive knowledge of machine learning algorithms, data modelling and simulation techniques

Proficiency in of the Cloud (Azure, GCP, AWS)

Strong leadership skills with a proven track record of mentoring and developing talent

Excellent communications skills, capable of conveying complex AI concepts to non technical stakeholders A strategic thinker with a passion for problem-solving and innovation

SME in statistics, analytics, big data, data science, machine learning, deep learning, cloud, mobile, and full stack technologies.

Hands-on experience analysing large amounts of data to derive actionable insights.

Working knowledge on traditional statistical model building (Example: Regression, Classification, Time series, Segmentation etc.), machine learning( Random forest, Boosting algos, SVM, KNN etc), deep learning(CNN, RNN, LSTM, Transfer learning) and NLP( Stemming, Lemitization, Named entity extraction, Latent semantic analysis etc).

Experience in tensorflow, Pytorch, Pytorch Lightning, etc, hugging Face, etc Aays Analytics | www.aaysanalytics.com

Ability to Pretrained transformers models, LLM, etc using Pytorch/Tensorlfow/Hugging Face using CPU/GPU. Aays Analytics | www.aaysanalytics.com