... remote-first environment. The role offers significant ownership, modern cloud-native tooling, and ... At least 5 years of experience in MLOps, DevOps, or related engineering roles supporting production ...
... remote-first environment. The role offers significant ownership, modern cloud-native tooling, and ... At least 5 years of experience in MLOps, DevOps, or related engineering roles supporting production ...
Remote Fastapi Developer information
What are the key skills and qualifications needed to thrive as a Remote FastAPI Developer, and why are they important?
To excel as a Remote FastAPI Developer, you need strong proficiency in Python programming, RESTful API design, and experience with the FastAPI framework, typically supported by a relevant degree or equivalent experience. Familiarity with tools such as Docker, Git, SQL/NoSQL databases, and cloud platforms like AWS or Azure is highly valued, and certifications in cloud or backend development can be advantageous. Excellent problem-solving, self-management, and communication skills are crucial for collaborating effectively in a remote environment. These skills ensure you can deliver robust, scalable APIs while efficiently working with distributed teams.
What is a Remote FastAPI Developer?
A Remote FastAPI Developer is a software engineer who specializes in building web APIs using the FastAPI framework, while working remotely from any location. FastAPI is a modern, high-performance Python web framework used to create APIs quickly and efficiently. Remote FastAPI Developers design, implement, and maintain backend services, typically collaborating with distributed teams through online communication and project management tools. Their responsibilities often include writing clean, scalable code, integrating databases, and ensuring API security and performance.
What are some common challenges Remote FastAPI Developers face when collaborating with distributed teams?
Remote FastAPI Developers frequently work with colleagues across different time zones and communication styles, which can make real-time collaboration and code reviews more challenging. Staying aligned on project requirements, API design standards, and deployment schedules often requires proactive communication and thorough documentation. Using tools like version control, issue trackers, and asynchronous messaging helps bridge these gaps, but developers must be disciplined about keeping everyone updated and clarifying technical decisions. Building strong remote working habits and establishing clear processes with your team can greatly improve collaboration and project outcomes.
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What are popular job titles related to Remote Fastapi Developer jobs in Missouri? For Remote Fastapi Developer jobs in Missouri, the most frequently searched job titles are:
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Full-time
Medical
Posted 8 days ago
Job description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a MLOps Engineer based in Netherlands.
Join a high-impact engineering team building the infrastructure that powers next-generation AI solutions for enterprise-scale decision-making. In this role, you will design, deploy, and optimize production-grade machine learning systems that support the full ML lifecycle, from training to inference. You'll collaborate with talented engineers to create highly scalable, reliable, and secure MLOps platforms capable of handling demanding workloads. This is an opportunity to solve complex technical challenges, improve model performance at scale, and contribute to cutting-edge AI technologies in a fast-paced, collaborative, and remote-first environment. The role offers significant ownership, modern cloud-native tooling, and the chance to shape the future of production AI systems.
Accountabilities
- Develop, automate, and maintain scalable machine learning pipelines, CI/CD workflows, and orchestration frameworks to support efficient model development and deployment.
- Design and implement high-performance model serving infrastructure using industry-standard serving frameworks while optimizing inference for low latency and high throughput.
- Build reliable deployment strategies including A/B testing, canary releases, rollback mechanisms, and production validation processes.
- Create robust monitoring, logging, alerting, and observability solutions to ensure model reliability, performance, and operational excellence.
- Optimize infrastructure utilization by improving GPU efficiency, enabling autoscaling, and managing cloud resources effectively.
- Design and maintain feature stores, scalable data pipelines, and storage architectures capable of supporting large-scale training and inference workloads.
- Collaborate with engineering teams to continuously improve platform scalability, security, governance, and operational best practices.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
- At least 5 years of experience in MLOps, DevOps, or related engineering roles supporting production machine learning environments.
- Proven experience designing and building MLOps infrastructure from the ground up using platforms such as MLflow, Weights & Biases, Kubeflow, or similar.
- Strong hands-on experience with machine learning frameworks including PyTorch and TensorFlow, as well as model serving technologies such as TorchServe, TensorFlow Serving, Triton, or KServe.
- Solid experience developing and managing scalable data pipelines, Kubernetes environments, cloud infrastructure (AWS, GCP, or Azure), and Infrastructure as Code solutions including Terraform, Helm, or GitOps.
- Strong programming skills in Python, Bash, and Go, with a focus on maintainable, scalable, and production-quality software.
- Knowledge of AI system security, model governance, compliance, monitoring, and observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Experience with FastAPI, Databricks, Snowflake, SRE practices, or cloud security certifications is considered an advantage.
- Competitive salary and equity package.
- Comprehensive healthcare coverage for employees and eligible dependents.
- Paid parental leave supporting all paths to parenthood, including adoption and surrogacy.
- Relocation assistance for employees joining one of the company's office locations where applicable.
- Fully remote work within Europe.
- Opportunity to work on cutting-edge AI technologies with significant technical ownership.
- Inclusive, collaborative, and mission-driven engineering culture focused on innovation, learning, and professional growth.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
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