MLOps Team Lead
At a glance
Senior ML Platform & Ops role at Fundamental. Remote, Israel · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Lead MLOps engineers and set their technical direction, while building strong operational practices across the team
- Set the MLOps roadmap with research, engineering and product, then make key calls on infrastructure design and tools
- Build scalable ML pipelines, CI/CD and orchestration, and guide training choices so models move smoothly into serving
- Develop feature stores, data pipelines and storage for large datasets, connecting research experiments with production systems
- Set monitoring for model quality, drift and system reliability, and shape inference plans using model and hardware needs
What you bring
- At least seven years in MLOps, including five years in a technical leadership position
- A computer science, engineering or related degree, or equivalent hands-on experience
- Strong Python engineering skills, plus experience with Bash or Go
- A record of building MLOps or infrastructure teams and creating their systems from the ground up
- Hands-on depth in MLOps platforms, model-serving frameworks, data pipelines and Kubernetes on a major cloud provider
Who this fits
This is a senior technical leadership role that combines team mentoring with hands-on ownership of ML infrastructure. It suits someone who can connect research decisions with production and serving needs. The role is remote.
From the employer
About Fundamental
Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.
At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.
Key responsibilities
Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence
Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives
Establish best practices, standards, and processes for ML infrastructure, deployment, and operations
Own technical decision-making for ML infrastructure architecture and tooling choices
Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
Partner with the model-serving team on serving infrastructure strategy (Triton, TorchServe, TensorFlow Serving, KServe), ensuring training-side decisions (checkpoint formats, export pipelines, resource footprint) don't create friction downstream
Collaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions
Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data
Collaborate with research teams to bridge the gap between experimentation and production
Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability
Must have
Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
7+ years of experience in MLOps, with 5+ years in a technical leadership role
Strong software engineering skills in Python, with experience in Bash and/or Go
Proven track record of building and leading high-performing MLOps or infrastructure teams
Experience building and designing MLOps infrastructure from the ground up
Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.)
Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference
Experience building and managing data pipelines to support both model training and inference
Good experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (Terraform, Helm, GitOps)
Proficient with observability and monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry)
Excellent communication skills with ability to translate between research and production contexts
Nice to have
Experience with workflow orchestration tools (Kubeflow, Airflow, Argo Workflows)
Experience with FastAPI and backend applications
Familiarity with data platforms like Databricks or Snowflake
Experience with LLM/foundation model serving and optimization
Exposure to SRE practices or cloud security certifications
Experience scaling ML infrastructure for AI startups
Benefits
Competitive compensation with salary and equity
Comprehensive health coverage for you and your dependents
Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
Relocation support for employees moving to join the team in one of our office locations
A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action