Machine Learning Engineer / MLOps Engineer

Archer; Denver (CO); Presencial; USD 140.000 - 180.000; Jornada completa

Descripción de la vacante

Archer is seeking a pragmatic ML Engineer to operationalize and scale ML models within its growing US Data team. You will own production deployments, build reliable inference endpoints on Vertex AI, and drive CI/CD/CT pipelines with a strong focus on automation and maintainability.

You will collaborate closely with the US AgentOps Team Lead and the Data Science Team Leader to accelerate deployment velocity while ensuring data and model quality across production systems.

Formación

• Proven experience as an ML Engineer, Data Engineer, or Software Engineer with production ML deployments.
• Pragmatic approach prioritising speed, reliability, and business value.
• Strong Python programming with API development and testing.
• Extensive hands-on experience with Google Cloud Platform (GCP).
• Vertex AI experience (Pipelines, Endpoints, Workbench).
• Experience with Docker and container orchestration.
• Excellent communication translating software concepts for data scientists.
• Experience with Terraform IaC.
• Experience with GKE container workloads.
• Familiarity with real-time streaming tools (Kafka/Pub/Sub).

Responsabilidades

• Own deployment of ML models to production with scalable endpoints on Vertex AI.
• Design, implement, and maintain CI/CD/CT pipelines for ML workflows.
• Set up automated monitoring and alerting for data drift and performance.
• Champion software engineering practices within the Data Science team.
• Collaborate with the Data Science Team Leader and AgentOps Team Lead to accelerate deployments.

Conocimientos

ML Engineer

Python

GCP

Vertex AI

Docker

GKE

Terraform

API development

CI/CD

Streaming (Kafka/Pub/Sub)

Communication

Herramientas

Vertex AI Pipelines

Cloud Build

Terraform

GKE

Kafka

Pub/Sub

Descripción del empleo

Archer is collaborating with Bet365 to connect them with exceptional professionals for this role.

We are seeking a highly pragmatic, results-driven Machine Learning (ML) Engineer to join our newly established US Data team. In this role, you will build the reliable, automated infrastructure that powers our machine learning lifecycle.

Your primary mission is to operationalize and scale the models developed by our data science team, taking them from prototype to robust, production-grade systems with high velocity.

You’ll focus on building reliable, automated and maintainable systems, keeping solutions pragmatic rather than over-engineered. You will also be passionate about automation, software engineering excellence, and MLOps.

You will report to the Data Science Team Leader and work in close alignment with the US AgentOps Team Lead (responsible for agentic and model orchestration platforms) and our UK technical excellence center. You will act as the bridge between model development and reliable platform engineering.

Preferred Skills And Experience

• Proven experience as an ML Engineer, Data Engineer, or Software Engineer with a clear focus on deploying, monitoring, and scaling machine learning systems in production.


• A pragmatic, proactive approach to system design, prioritizing speed, reliability, and business value over complex, theoretical infrastructure.


• Strong Python programming skills, with a solid grasp of software engineering patterns, API development, and automated testing frameworks.


• Extensive hands‑on experience with Google Cloud Platform (GCP).


• Practical experience with Vertex AI (specifically Vertex AI Pipelines, Endpoints, and Workbench).


• Proficiency with containerization (Docker) and container orchestration tools.


• Excellent communication skills, with the ability to translate software engineering concepts for data scientists and operational requirements for product leads.


• Experience utilizing Infrastructure as Code (IaC) tools such as Terraform.


• Experience running containerized workloads on Google Kubernetes Engine (GKE).


• Familiarity with real‑time streaming tools like Apache Kafka or GCP Pub/Sub.

What you will be doing

• Owning the deployment of machine learning models to production. Build and maintain scalable, low‑latency prediction endpoints using GCP Vertex AI.


• Designing, implementing, and maintaining CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows using Vertex AI Pipelines, Cloud Build, and related GCP tools.


• Setting up automated monitoring and alerting frameworks (e.g., Vertex AI Model Monitoring) to track data drift, model drift, and system performance in real‑time.


• Championing best practices for software engineering within the Data Science team, including robust unit testing, containerization, version control, and CI/CD automation.


• Working closely with the Data Science Team Leader, Junior Data Scientists, and the AgentOps Team Lead to accelerate deployment cycles, remove operational bottlenecks, and maintain high deployment velocity.