Principal Artificial Intelligence Engineer - Data Modeling

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