AI/ML Engineer
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Overview
As an AI/ML Engineer at Pythian, you design, build, and maintain scalable AI/ML pipelines for client and internal use. You deploy and optimize models, including LLMs and Generative AI, across production environments and cloud platforms. You collaborate with data scientists and software engineers to deliver production-ready AI systems, emphasizing performance, cost-efficiency, and maintainability. This role combines hands-on engineering with shaping AI solutions for transformative outcomes within a cloud-focused services company.
Compensaciones / Beneficios competitive total rewards
remote work options
training allowance and professional development days
wellness budget
paid vacation and sick days
charitable volunteering day
Responsabilidades Develop, deploy, and maintain AI/ML pipelines for internal and client-driven projects
Deploy, manage, and scale AI models (LLMs and custom models) into production
Translate model prototypes into scalable, production-ready AI systems
Optimize model performance, latency, and cost on cloud platforms
Integrate AI/ML solutions with AWS, GCP, Azure and use Docker/Kubernetes for consistent deployment
Apply MLOps practices: CI/CD, model versioning, monitoring, maintenance
Coordinate with software engineers to embed AI capabilities into applications and workflows
Stay updated on AI/ML tech, Generative AI, and MLOps deployment strategies
Requisitos principales Bachelor's or Master's degree in Computer Science, Engineering, AI or related quantitative field
4 to 5 years of experience in ML engineering or ML/AI-focused software development
1-3 years experience with ADK or other agentic frameworks
Strong Python programming skills
Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Hands-on experience deploying/pre-trained models (LLMs/Generative AI) into production
Cloud platform experience (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes)
Solid knowledge of Data Engineering, ETL/ELT, and Git
Experience with Kubeflow or managed ML tools
Experience building/scaling AI/ML systems
Familiarity with MLOps practices (monitoring, logging, CI/CD)
Strong communication and cross-functional collaboration skills
Strong communication
Team collaboration across cross-functional teams
Problem-solving orientation
Python
TensorFlow
PyTorch
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