AI/ML Engineer
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Job Description
AI / ML Engineer at Pythian developing robust machine learning pipelines. Integrating AI models into client solutions and optimizing performance on cloud platforms.
Responsibilities:
- Develop, deploy, and maintain robust AI and machine learning pipelines for internal and client-driven projects.
- Deploy, manage, and scale AI models, including pre-trained models (e.g., LLMs) and custom ML models, into 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 major cloud platforms (AWS, GCP, Azure) and utilize containerization technologies (Docker, Kubernetes) for consistent deployment.
- Apply standard MLOps practices, including continuous integration/continuous delivery (CI/CD), model versioning, monitoring, and maintenance systems.
- Collaborate with software engineering teams to ensure seamless integration of AI capabilities into applications and user workflows.
- Stay up to date with advancements in AI/ML technologies, Generative AI, and MLOps deployment strategies.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related quantitative field.
- 2 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 and proficiency in ML/AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience in deploying and working with pre-trained models, such as Large Language Models (LLMs) or similar Generative AI technologies, into production environments.
- Solid experience with cloud platforms (AWS, GCP, Azure) and container orchestration using Docker and Kubernetes.
- Solid understanding of Data Engineering principles, ETL/ELT processes, and version control systems (e.g., Git).
- Experience in orchestrating Machine Learning pipelines using open-source tools like Kubeflow or managed cloud services.
- Experience in 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 collaboration skills, with an ability to work effectively across cross-functional teams including solution architects and data scientists.
Benefits:
- Competitive total rewards package.
- Blog during work hours; take a day off and volunteer for your favorite charity.
- Flexibly work remotely from your home, there’s no daily travel requirement to an office!
- Hone your skills or learn new ones with our substantial training allowance; participate in professional development days, attend training, become certified, whatever you like!
- We give you all the equipment you need to work from home including a laptop with your choice of OS, and an annual budget to personalize your work environment!
- You will have an annual wellness budget to make yourself a priority (use it on gym memberships, massages, fitness and more).
- Generous amount of paid vacation and sick days, as well as a day off to volunteer for your favorite charity.
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