$79K - $104K/yr
... engineering, research, and product teams. Working in a fully remote, international environment, you ... Experience with workflow orchestration tools, FastAPI, Databricks, Snowflake, LLM infrastructure ...
New
$79K - $104K/yr
... engineering, research, and product teams. Working in a fully remote, international environment, you ... Experience with workflow orchestration tools, FastAPI, Databricks, Snowflake, LLM infrastructure ...
New
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.
What are the most commonly searched types of Fastapi Developer jobs in Missouri? The most popular types of Fastapi Developer jobs in Missouri are:
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:
What job categories do people searching Remote Fastapi Developer jobs in Missouri look for? The top searched job categories for Remote Fastapi Developer jobs in Missouri are:
What cities in Missouri are hiring for Remote Fastapi Developer jobs? Cities in Missouri with the most Remote Fastapi Developer job openings:
$79K - $104K/yr
Full-time
Medical
Posted 7 days ago
New
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 Lead based in Netherlands.
As an MLOps Lead, you will shape the strategy, architecture, and operational excellence of a cutting-edge machine learning infrastructure supporting large-scale AI systems. Leading a team of MLOps engineers, you will bridge the gap between research and production, ensuring that machine learning models are deployed, monitored, and scaled efficiently in high-performance environments. This role combines technical leadership with hands-on architectural decision-making, offering the opportunity to build robust infrastructure from the ground up while collaborating closely with engineering, research, and product teams. Working in a fully remote, international environment, you will help establish best practices and drive innovation across the entire machine learning lifecycle, enabling the delivery of reliable and scalable AI solutions.
Accountabilities
- Lead, mentor, and develop a high-performing team of MLOps engineers while fostering a culture of collaboration, technical excellence, and continuous improvement.
- Define and execute the MLOps roadmap, aligning infrastructure initiatives with research, engineering, and product objectives.
- Design, implement, and maintain scalable machine learning infrastructure, including automated training pipelines, CI/CD workflows, orchestration frameworks, and deployment processes.
- Drive architectural decisions for model serving platforms, ensuring low-latency, high-throughput inference using modern serving technologies.
- Build and optimize feature stores, data pipelines, and storage solutions that support large-scale model training and production inference.
- Collaborate closely with research teams to streamline the transition of machine learning models from experimentation to production environments.
- Establish monitoring, logging, alerting, and observability strategies to ensure model performance, system reliability, and early detection of drift or operational issues.
- Define engineering standards, operational best practices, and scalable infrastructure processes that support long-term platform growth.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- Minimum of 7 years of experience in MLOps or machine learning infrastructure engineering, including at least 3 years in a technical leadership role.
- Strong software engineering expertise in Python, with working knowledge of Bash and/or Go.
- Proven experience building, scaling, and leading MLOps infrastructure from the ground up.
- Deep knowledge of machine learning platforms and frameworks such as MLflow, Weights & Biases (W&B), PyTorch, and TensorFlow.
- Extensive experience with model serving technologies including Triton Inference Server, TorchServe, TensorFlow Serving, or KServe.
- Hands-on expertise with Kubernetes, cloud platforms (AWS, GCP, or Azure), infrastructure as code tools (Terraform, Helm, GitOps), and production-grade data pipelines.
- Strong experience with monitoring and observability solutions such as Prometheus, Grafana, Datadog, and OpenTelemetry.
- Excellent communication skills with the ability to collaborate effectively across research and engineering teams.
- Experience with workflow orchestration tools, FastAPI, Databricks, Snowflake, LLM infrastructure, SRE practices, or AI startup environments is considered an advantage.
- Competitive compensation package including salary and equity participation.
- Comprehensive healthcare coverage for employees and eligible dependents.
- Generous paid parental leave supporting biological, adoptive, and surrogate parenthood.
- Relocation assistance for employees joining one of the company's office locations, where applicable.
- Fully remote work environment with international collaboration opportunities.
- Opportunity to lead cutting-edge AI infrastructure initiatives with significant technical ownership.
- Inclusive, mission-driven culture that values innovation, collaboration, diversity of thought, and continuous learning.
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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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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