MLOps Engineer
Join Fundamental as an MLOps Engineer to tackle technical challenges in AI and transform enterprise decision-making.
Posted by employer 3 months ago
First seen on Joblaze 1 month ago
Last verified on the company career page 21 hours ago
Skills & Technologies
Requirements
- Experience
- 5+ years
- Education
- Bachelor's degree
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the MLOps Engineer at Fundamental focuses on developing and managing automated machine learning pipelines and robust model serving infrastructures to enhance enterprise decision-making. Key skills include expertise in MLOps platforms, model serving frameworks, and cloud infrastructure, particularly with Kubernetes and major cloud providers. This position is ideal for seasoned professionals with a strong background in software engineering and MLOps, looking to contribute to a pioneering AI company. Fundamental's mission-driven culture emphasizes innovation and collaboration, making it a compelling environment for tech-savvy individuals.
Joblaze insights
Quick facts
- How much experience is required?
- At least 5 years of relevant experience for this MLOps Engineer role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Azure, Datadog, GCP, GitOps, Go.
- What seniority level is this role?
- Fundamental targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this MLOps Engineer role at Fundamental.
From the original posting
Key responsibilities
Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
Develop scalable inference architectures optimized, with ultra-low latency and high throughput
Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
Develop logging, alerting, and monitoring solutions to track model development, and reliability
Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data
Must have
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)
Experience building and designing MLOps infrastructure from the ground up
Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference
Experience in building and managing data pipelines to support both model training and inference
Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps)
Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code
Experience in AI/ML systems security, compliance, and model governance
Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry
Nice to have
Experience with ML workflow tooling (MLflow, Kubeflow, or similar)
Experience with FastAPI and Backend applications
Familiarity with data platforms like Databricks or Snowflake
Exposure to SRE practices or cloud security certifications
Hands-on experience with Prometheus, Grafana, or Datadog
Benefits
Competitive compensation with salary and equity
Standard company text repeated across Fundamental's postings is omitted here.