Senior ML Ops Engineer — Remote (AWS, Data Infra)

Jobgether; United States; A distancia; USD 151.000 - 173.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote position in the US

Healthcare coverage

401(k) with employer matching

Paid leave

Parental leave

Life insurance

Transit benefits

Pet health insurance

Wellness program

Descripción de la vacante

Jobgether is seeking a seasoned MLOps/platform engineering professional to design, deploy, and operate AWS-based ML and AI infrastructure. You will build event-driven integrations, CDC pipelines with Salesforce/Braze, and secure, scalable inference APIs. The role emphasizes IaC, observability, and collaboration with Data Science to productionize models.

You will lead incident response, scale reliable systems, and drive safe deployment practices across multiple environments in a remote US setting.

Formación

• Six+ years in MLOps, platform engineering, DevOps, SRE, or related disciplines in production ML or data workloads.
• Bachelor's degree in a technical field.
• Deep hands-on AWS production workload experience across compute, messaging, security.
• Strong Python for production APIs and backend services.
• Experience building event-driven integrations with third-party APIs and CDC pipelines.
• Terraform and IaC across multiple environments.
• Practical experience deploying ML models with batch/real-time serving and monitoring.
• Experience with containerization, CI/CD, and safe production deployment practices.
• Hands-on PostgreSQL production experience, including migrations and indexing.
• Strong reliability, security, observability, and operational support knowledge.
• Ability to work independently, own outcomes, and make decisions.
• Excellent communication and cross-functional collaboration skills.
• Strong organizational and analytical skills to manage multiple projects.
• Preferred: experience with internal LLM/agent systems, ML observability, Snowflake workloads, and regulated industries.

Responsabilidades

• Design, build, deploy, and operate AWS infrastructure for ML models and AI tools.
• Develop event-driven integrations and CDC pipelines with Salesforce and Braze.
• Design reliable integration workflows with idempotency, replay, and schema evolution.
• Build and maintain containerized inference APIs and backend services with secure interfaces.
• Manage infrastructure as code with Terraform across AWS accounts and environments.
• Develop ML workflows for training, retraining, scoring, batch processing, and real-time inference.
• Collaborate with Data Science to move models from experimentation to production.
• Establish observability with logging, metrics, alerts, and model monitoring.
• Support secure operation of internal LLM and agentic systems with access controls and auditing.
• Collaborate with Data Engineering on Snowflake datasets and governance for ML workloads.
• Operate PostgreSQL-backed production services with migrations and indexing under concurrency.
• Lead incident response, on-call rotations, and runbooks to improve readiness.
• Establish platform engineering standards and safe deployment practices.
• Work cross-functionally to resolve integration challenges and maintain scalable AI infrastructure.

Conocimientos

MLOps

Platform engineering

DevOps

SRE

Python

AWS

Event-driven architectures

Terraform

CI/CD

PostgreSQL

Security

Observability

Educación

Bachelor's degree in a relevant technical field

Herramientas

Snowflake

Salesforce

Braze

Amazon Bedrock

Descripción del empleo

Jobgether is seeking a seasoned MLOps/platform engineering professional to design, deploy, and operate AWS-based ML and AI infrastructure. You will build event-driven integrations, CDC pipelines with Salesforce/Braze, and secure, scalable inference APIs. The role emphasizes IaC, observability, and collaboration with Data Science to productionize models.

You will lead incident response, scale reliable systems, and drive safe deployment practices across multiple environments in a remote US setting.