i
iCapital
DevOps Engineer Senior Vice President
Skills
PythonPostgreSQLGitLabMachine LearningAIAWSLinuxKubernetesComplianceTerraform
2 months ago
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Important information
Contract type:
Permanent
Salary:
$180,000 – $230,000 / year
Location:
New York, NY, USA
Work mode:
Onsite
Published on:
12 August 2026
What they need
- The Platform Infrastructure team at iCapital plays a critical role in ensuring that both production and development environments operate smoothly, securely, and reliably
- This role leverages advanced cloud capabilities to support the Platform Infrastructure strategy of market agility and lean operating principles, with a strong emphasis on quality to meet the ever‑growing demands of our clients
- We are seeking highly collaborative, creative, and intellectually curious MLOps/DevOps Engineers with deep expertise in machine learning operations, cloud infrastructure, CI/CD automation, Kubernetes, and security
- This role requires hands‑on experience designing, building, and operating scalable DevOps and enterprise‑grade MLOps platforms, including model lifecycle automation, observability, and governance
- As a Platform Engineer, you will wear multiple hats in a highly visible role, partnering closely with engineering, security, data, and business teams to deliver secure, reliable, and highly automated platforms that support both application and machine‑learning workloads
- Design, build, and operate MLOps pipelines supporting the full ML lifecycle (training, validation, deployment, monitoring)
- Enable production workloads for AI/ML and Generative AI systems, including LLM‑based services
- Develop and maintain CI/CD pipelines for AI/ML services and supporting infrastructure
- Build and manage cloud‑native infrastructure on AWS, with heavy use of Kubernetes and containerized workloads
- Automate infrastructure provisioning and configuration using Infrastructure as Code (Terraform)
- Implement model versioning, experiment tracking, and artifact management across environments
- Ensure reliability, scalability, observability, and cost efficiency of AI platforms
- Partner with AI/ML engineers to operationalize models and standardize deployment patterns
- Implement monitoring and alerting for system health, model performance, and drift
- Enforce security, compliance, and governance requirements for AI workloads
- Participate in incident response, root cause analysis, and continuous improvement initiatives
- Document standards, best practices, and reference architectures for MLOps and AI infrastructure- Background in cost optimization for compute‑intensive workloads
- Hands‑on experience with machine learning platforms, particularly AWS SageMaker (required)
- AWS certifications are a plus
- Proven experience building and operating CI/CD pipelines (e.g., GitLab CI, ArgoCD)
- Direct experience with MLOps platforms and tooling (model registries, experiment tracking, feature stores)
- Experience supporting machine learning systems in production, including deployment and monitoring concerns
- Excellent communication skills and ability to work across teams
- Strong hands-on experience with Kubernetes, containerized workloads, and cloud networking
- Familiarity with data stores commonly used in ML systems (e.g., Postgres, DynamoDB, object storage)
- 15+ years of experience in DevOps, SRE, or Platform Engineering, with AWS as a primary cloud
- Strong written and verbal communication skills
- Exposure to Generative AI / LLM workloads in production environments
- Experience operating in regulated or fintech environments
- Solid Linux, systems, and troubleshooting fundamentals
- Strong proficiency with Terraform and scripting/programming in Python or similar languages
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