MLOps Engineer Recruiters

What this hire really comes down to

The MLOps Engineer is the architect of the infrastructure that allows machine learning models to thrive in production environments. You are responsible for creating the systems that bridge the divide between training notebooks and scalable, monitorable services. This role demands a high level of expertise in container orchestration, data versioning, and the automation of complex workflows that keep models relevant and performant despite fluctuating real-world data distributions.

Your mission is to remove the friction from the ML lifecycle, enabling data scientists and ML engineers to iterate with speed and safety. Your hire will manage the complexities of model deployment, infrastructure cost-efficiency, and system reliability, ensuring that the underlying hardware is utilized optimally. By building resilient platforms that integrate seamlessly with CI/CD paradigms, you empower our partners to scale their AI capabilities without sacrificing the integrity of their production systems.

What we check before you see anyone

• Kubernetes Mastery

  Kubernetes is the engine of modern ML infrastructure, and we expect deep expertise in managing containerized workloads. We evaluate your experience in configuring resource requests and limits, managing auto-scaling, and setting up service meshes to support ML services. You should be prepared to discuss how you ensure high availability and load balancing for inference endpoints while maintaining strict security boundaries within a shared multi-tenant cluster.

• Model Registry (MLflow)

  Managing the lifecycle of models requires a disciplined approach to versioning and lineage. We look for proficiency in using model registries to track experiment metadata, manage model artifacts, and control the transition of models from staging to production. You must demonstrate how you use these tools to enforce reproducibility and auditability, ensuring that every deployed model can be mapped back to its training source and configuration.

• Feature Stores

  Your experience with feature stores is critical for maintaining consistency between online and offline data. We look for evidence that you understand how to build and maintain feature pipelines that support both real-time inference and historical batch processing. A successful candidate demonstrates how they handle data skew, feature engineering bottlenecks, and the synchronization required to ensure that production models receive the exact inputs they expect.

• CI/CD for Models

  We evaluate the candidate's ability to treat machine learning models like software code. This includes implementing automated testing suites for data quality, model validation, and deployment pipelines that trigger on code or data changes. The candidate needs to explain how you handle rollbacks, canary deployments for models, and the automated monitoring gates that prevent sub-optimal or erroneous models from reaching production and impacting the business.

• GPU Scheduling

  Optimizing expensive hardware is a core requirement for this role. We look for your experience in scheduling GPU workloads to ensure maximum utilization and performance. You must understand the complexities of GPU-sharing, managing driver compatibility, and configuring resource allocation for varying model weights. Candidates should show they can identify and address potential bottlenecks such as memory fragmentation or data transfer latency that hinder training throughput.

• Infrastructure as Code

  Infrastructure should be reproducible and documented through code. We assess the candidate's proficiency in using automation tools to manage cloud resources, environments, and configurations. The candidate needs to demonstrate your experience in building scalable, secure environments that follow infrastructure-as-code principles. Candidates must prove they can automate the entire lifecycle of an ML project from initial provisioning through tear-down, ensuring consistency across development, staging, and production environments.

Where we find them

Our sourcing efforts focus on systems engineers and platform experts who have specialized in the infrastructure needs of machine learning teams. We prioritize candidates with backgrounds in site reliability engineering or distributed systems who have successfully transitioned into the MLOps niche. By connecting with practitioners who have built platform teams within scaling tech companies, we identify individuals who understand the operational realities of supporting hundreds of concurrent ML experiments.

We also recruit from communities responsible for Kubernetes and cloud-native infrastructure, where the focus is on performance and scale. These individuals often have deep experience in managing complex compute resources and building automated pipelines that support developers. By targeting those who have mastered the intricacies of hardware-software integration, we find engineers who can build the stable foundations required for advanced model development and long-term production success.

Mistakes we see

• Treating a credential as the answer

  Do not let kubernetes mastery become a keyword filter. You should be prepared to discuss how you ensure high availability and load balancing for inference endpoints while maintaining strict security boundaries within a shared multi-tenant cluster.

• Leaving the actual scope out

  Put the responsibility for model registry (mlflow) in the brief, including who helps the hire and who decides. Managing the lifecycle of models requires a disciplined approach to versioning and lineage.

• Waiting until the offer to discuss the work

  Discuss what the person will own in feature stores before the interview loop. A successful candidate demonstrates how they handle data skew, feature engineering bottlenecks, and the synchronization required to ensure that production models receive the exact inputs they expect.

The search in three steps

1. Agree the brief

   The evaluation focuses on the candidate's ability to build infrastructure that is both resilient and developer-friendly. We begin with a deep dive into your experience with container orchestration and the specific challenges of managing data-intensive workloads. Your hire will be asked to discuss your past projects where you designed CI/CD pipelines for models and handled significant scaling hurdles, highlighting your approach to balancing automation with system stability and developer productivity.

2. Find and speak with people

   The final technical assessment involves a practical challenge related to designing a reliable ML platform architecture. Your hire will be expected to make architectural decisions regarding resource management, model serving, and data governance. We evaluate the candidate's ability to document and explain the candidate's design choices, as well as your capacity to anticipate failure modes in production. We look for engineers who possess both the operational rigor of an SRE and the specialized knowledge of modern ML ecosystems.

3. Introduce the people who fit

   We send a written brief on each person you meet, including why the work fits and the questions still worth asking about infrastructure as code. You decide who to interview. We help with the conversations through the offer.

Questions about this search

Where we fill this role

• SaaS and B2B Software
• Fintech, Payments and Insurtech
• Banking, Capital Markets and Trading
• Healthtech and Digital Health
• AI-Native Companies
• PE-Backed Software
• Enterprise and Fortune 1000
• Ecommerce, Retail and Marketplaces
• Govtech, Defense and Aerospace (Cleared)
• Cybersecurity Vendors
• Energy, Utilities and Climate Tech
• Consulting, MSPs and Systems Integrators
• Media, Gaming and Consumer Apps

Related roles and stacks

• AI, Machine Learning and Data Science
• Machine Learning Engineer
• AI Engineer
• Data Scientist
• Research Scientist / Applied Scientist
• Computer Vision Engineer
• Quantitative Developer / Researcher
• Python
• C++
• GCP
• Kubernetes
• Databricks

Hiring guidance

Plan this hire

• Positions we recruit
• Team hiring guide
• Head of AI
• Platform Engineer, DevOps Engineer or SRE?
• Help desk tiers: where escalation should go
• Moving from an MSP to in-house IT
• Credential checks