On-site
Full Time
• 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.
• 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
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