
201 - 500 employees
Founded 1997
💼 Consulting
🏥 Healthcare
🏭 Manufacturing
💰 $15M Venture Round on 2017-05
Consulting • Healthcare • Manufacturing
Pythian is a global data and analytics services company that specializes in helping organizations transform by leveraging data, analytics, AI, and the cloud. Pythian offers services in database management, cloud solutions, digital workplaces, and enterprise applications, working with partners like AWS, Google, and Microsoft. The company enables clients to optimize their data estates, secure their data, and drive better business outcomes through advanced analytics and artificial intelligence. Pythian serves a variety of industries, including financial, healthcare, manufacturing, retail, and education, providing tailored solutions that enhance operational efficiency, security, and innovation.
🕒 August 27
Where this job comes from
Posted on Pythian's careers page, not through a job board
First seen by Remote Rocketship August 27
Still open on Pythian's site. Checked 12 hours ago.

201 - 500 employees
Founded 1997
💼 Consulting
🏥 Healthcare
🏭 Manufacturing
💰 $15M Venture Round on 2017-05
Consulting • Healthcare • Manufacturing
Pythian is a global data and analytics services company that specializes in helping organizations transform by leveraging data, analytics, AI, and the cloud. Pythian offers services in database management, cloud solutions, digital workplaces, and enterprise applications, working with partners like AWS, Google, and Microsoft. The company enables clients to optimize their data estates, secure their data, and drive better business outcomes through advanced analytics and artificial intelligence. Pythian serves a variety of industries, including financial, healthcare, manufacturing, retail, and education, providing tailored solutions that enhance operational efficiency, security, and innovation.
• Develop, deploy, and maintain robust AI and machine learning pipelines for internal and client-driven projects • Deploy, manage, and scale pre-trained and custom AI/ML models in production environments • Work with data scientists to implement model prototypes into scalable, production-ready AI systems • Optimize and tune model performance, latency, and cost-efficiency on cloud platforms • Integrate AI/ML solutions with AWS, GCP, and Azure • Use Docker and Kubernetes for consistent deployment • Apply MLOps practices, including CI/CD, model versioning, monitoring, and maintenance systems • Coordinate with software engineering teams to integrate AI capabilities into applications and user workflows • Stay current with AI/ML, Generative AI, and MLOps deployment advancements
• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related quantitative field • 4 to 5 years of progressive experience in machine learning engineering, software development with an ML/AI focus, or a related role • Strong programming skills in Python • Proficiency in TensorFlow, PyTorch, or Scikit-learn • Hands-on experience deploying and working with pre-trained models, including Large Language Models (LLMs) or similar Generative AI technologies, in production environments • Solid experience with AWS, GCP, or Azure • Experience with Docker and Kubernetes container orchestration • In-depth knowledge of Data Engineering principles and ETL/ELT processes • Experience with version control systems such as Git • Experience orchestrating Machine Learning pipelines using Kubeflow or managed cloud services • Experience building and optimizing scalable AI/ML systems • Familiarity with MLOps best practices, including model monitoring, logging, and CI/CD pipelines for AI assets • Strong communication and teamwork skills • Ability to work effectively across cross-functional teams, including solution architects and data scientists • Successful applicant must fulfill requirements necessary to obtain a background check
• Competitive total rewards package • Blog during work hours • Day off to volunteer for your favorite charity • Flexible remote work from home with no daily travel requirement to an office • Substantial training allowance • Professional development days • Training and certification opportunities • Equipment for working from home, including a laptop with choice of OS • Annual budget to personalize your work environment • Annual wellness budget • Paid vacation days • Paid sick days
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