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Company Overview
A global technology leader renowned for innovation, design, and a vast ecosystem of hardware, software, and services. They are dedicated to creating products that empower and enrich people’s lives.
In this role, the successful candidate will concentrate on these vital areas:
Model Management: Oversee the deployment, maintenance, and scaling of LLM and associated services within consumer-facing products.
CI/CD: Develop and maintain CI/CD pipelines to automate the processes of model training, testing, deployment, and scaling.
Collaborative Integration: Work closely with product developers, UX designers, and data scientists to ensure the smooth and intuitive integration of language models into their products.
Continuous Monitoring: Establish dashboards and regularly monitor model performance, guaranteeing consistent accuracy and reliability for end-users. Implement alerts and manage the necessary monitoring types.
Feedback Integration: Devise strategies for gathering user feedback and using it to refine the model, thereby improving alignment with consumer needs.
Bias Mitigation: Proactively identify and mitigate potential biases in model predictions, ensuring their products are inclusive and fair.
Infrastructure Management: Design and implement efficient data pipelines to facilitate the training and inference of large language models.
Documentation: Maintain thorough documentation encompassing model versions, deployment procedures, and performance metrics.
Research & Development: Stay informed about the latest advancements in large language models and MLOps, ensuring their consumer products remain at the cutting edge of innovation.
Minimum Qualifications
M.S. in a relevant field with over 3 years of experience applying AIOps machine learning engineering principles to address real-world business challenges.
Language Model Expertise: Demonstrated experience working with large language models, such as OpenAI’s GPT series or comparable models.
MLOps Skills: Strong background in automating ML pipelines, including the training, testing, deployment, monitoring, and scaling of AI models.
Technical Toolkit: Proficiency in PyTorch, TensorFlow, Transformers, Kubernetes, Docker, LangChain, vectorDB, and cloud platforms like AWS, GCP, or Azure, along with monitoring tools such as Grafana, CI/CD tools like Airflow and GitLab, and Big Data management tools like Spark and Kafka.
Product-Centric Mindset: A proven history of successfully integrating AI models into consumer products, enhancing both functionality and user experience.
Preferred Qualifications
Communication Prowess: Excellent communication abilities, capable of effectively communicating between technical and non-technical teams.
Collaborative Spirit: Experience working within cross-functional teams, ensuring seamless product development processes.
Disclaimer
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No Client Relationship: This company is not a client of SpringCube unless stated.
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