**This is an in-office role - please do not apply if are not located in NYC**
**If you have previously applied to Domify, rest assured that we have seen your application and will be in touch if your profile fits this role. Do not re-apply.**
About Domify
Domify is seeking an experienced Dev Ops / ML Ops engineer help us build an enterprise AI platform in partnership with our clients, the founding engineering team, and the management team.
Domify tackles the complex world of compliance and regulations in financial services, starting with wealth management. We leverage public and private data sources, proprietary workflows and compliance manuals, and the latest in Gen AI advancements to bring the power of AI to legal, compliance and regulatory oversight.
This role is a key hire for us as we build out our platform in partnership with lead clients. You will help us deploy our industry-leading AI platform to enterprise clients in a unique, new to industry go-to-market model.
Location: NYC in-office
We expect the successful candidate to work from Domify's mid-town office NYC 4 days a week. Telecommuting, short NYC stays or relocation are not in consideration at this time.
Salary
$130,000 to $150,000 + plus substantial equity, based on experience.
Desired Skills and Qualifications
Key Responsibilities:
- Design, implement, and maintain scalable cloud-based infrastructure for enterprise AI solutions.
- Develop and manage CI/CD pipelines pipelines for software and large language models.
- Automate deployment, monitoring, and scaling processes using DevOps and MLOps best practices.
- Implement security, compliance, and governance measures for enterprise AI deployments.
- Troubleshoot and resolve issues related to infrastructure, deployment, and ML pipelines.
- Work with enterprise clients to ensure seamless integration and deployment of our AI platform.
- Ensure high availability, fault tolerance, and disaster recovery strategies for AI workloads.
Qualifications & Experience:
- 3-5 years of experience in DevOps and MLOps roles, preferably in enterprise environments.
- Strong expertise in cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes).
- Hands-on experience with infrastructure as code (Terraform, CloudFormation, or similar).
- Experience building and managing CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI/CD, or similar).
- Solid understanding of ML model lifecycle management and MLOps frameworks (MLflow, Kubeflow, SageMaker, or similar).
- Proficiency in monitoring and logging tools (Prometheus, Grafana, ELK Stack, or similar).
- Strong scripting and programming skills (Python, Bash, or similar).
- Experience with enterprise AI implementations and customer-facing technical support is a plus.