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Job description
SVP, Lead AI/MLOps Infrastructure Engineer – Full Time – Hybrid
We’re partnering with our client, a fast-growing fintech firm, on a senior-level hire to lead and own the infrastructure behind their AI and machine learning platforms.
This is a highly visible, hands-on leadership role where you’ll own the end-to-end AI/ML platform stack, from training and inference infrastructure to model serving, reliability, and cost, while setting the MLOps roadmap and standards for the team.
The priority here is MLOps and AI infrastructure first , built on deep AWS , Terraform , and production ML / GenAI experience.
What You’ll Be Doing
Own the end-to-end AI/ML platform stack: orchestration, compute (including GPU), storage, and model serving
Build and operate MLOps pipelines across the full model lifecycle: training, validation, versioning, and deployment
Productionize AI/ML and GenAI (LLM) workloads in partnership with ML engineers and data scientists
Design and manage cloud-native AWS infrastructure using Kubernetes, and own Infrastructure as Code standards (Terraform)
Own SLAs/SLOs for model serving and inference; lead monitoring, drift detection, and incident response
Build internal tooling and standardized environments that boost ML engineer productivity (e.g., MLflow, Kubeflow, Weights & Biases, Ray)
Drive data governance, privacy compliance, and cost optimization across training and inference workloads
Set the MLOps roadmap and mentor engineers on infrastructure and MLOps best practices
What They’re Looking For
15+ years of experience in DevOps, SRE, or platform engineering, with AWS as primary cloud
Proven, hands-on experience building and operating MLOps pipelines in production (key priority)
Experience with MLOps tooling, including model registries, experiment tracking, and feature stores
Exposure to Generative AI / LLM workloads, including AWS Bedrock
Strong Infrastructure as Code (Terraform) and scripting skills (Python or similar)
Solid Linux, systems, and troubleshooting fundamentals
Excellent communicator, comfortable collaborating across teams
Nice to Have
Hands-on Kubernetes, containerized workloads, and cloud networking
Experience in regulated or fintech environments
Background optimizing costs for compute-intensive (GPU) workloads
Why This Role
Own and shape the AI platform at a growing fintech
High-impact leadership role setting MLOps strategy and standards
Strong compensation: $200K–$230K base + bonus + equity
Comprehensive benefits, including retirement match and unlimited PTO
Hybrid model: 4 days onsite / 1 day remote (NYC area)
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Worksite address
new york, NY, 10261, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.