12 days ago
Remote, PortugalMid Level / Senior
Responsibilities
- Develop, deploy, maintain, and scale AI/ML pipelines and production models for internal and client projects.
- Integrate and optimize LLMs, Generative AI technologies, and custom ML models in production environments.
- Convert data science prototypes into scalable, production-ready AI systems.
- Optimize model performance, latency, and cost efficiency on cloud platforms.
- Deploy AI/ML solutions across AWS, GCP, and Azure using Docker and Kubernetes.
- Apply MLOps practices including model versioning, monitoring, logging, maintenance, and CI/CD for AI assets.
- Coordinate with software engineering teams, solution architects, and data scientists to integrate AI capabilities into applications and workflows.
- Stay current with AI/ML, Generative AI, and MLOps deployment advances.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related quantitative field.
- 4 to 5 years of experience in machine learning engineering, ML-focused software development, or a related role.
- 1 to 3 years of experience with ADK or other agentic frameworks.
- Strong Python programming skills and proficiency with TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience deploying pre-trained models such as LLMs or similar Generative AI technologies to production.
- Experience with AWS, GCP, Azure, Docker, and Kubernetes.
- Knowledge of data engineering principles, ETL/ELT processes, and Git.
- Experience orchestrating ML pipelines with Kubeflow or managed cloud services.
- Experience building and optimizing scalable AI/ML systems.
- Familiarity with MLOps practices, model monitoring, logging, and CI/CD pipelines for AI assets.
- Strong communication and teamwork skills across cross-functional teams.
Benefits
- Flexible remote work from home with no daily office travel requirement.
- Competitive total rewards package, excluding unspecified compensation details.
- Training allowance, professional development days, training opportunities, and certification support.
- Company-provided work-from-home equipment, including a laptop with a choice of operating system.
- Annual budget for personalizing the work environment.
- Annual wellness budget, paid vacation and sick days, and a paid day to volunteer for a charity.
- Blog during work hours and collaborate with cross-functional industry experts.
Tech Stack
Categories
