Senior Platform Engineer

UnifyCX; United States; Remote; USD 100,000 - 140,000; Full time

Job summary

Join a forward-thinking company as a Senior Platform Engineer, where you'll design and scale innovative solutions that redefine customer experience. This full-time role offers the chance to work with cutting-edge technology in generative AI, contributing to scalable micro-service architectures and deploying systems efficiently on Google Cloud Platform. Collaborate with cross-functional teams to deliver impactful solutions while maintaining high coding standards. If you're passionate about MLOps and DevOps, this is the perfect opportunity to grow your career in a dynamic environment.

Qualifications

• 7+ years in DevOps roles focusing on cloud infrastructure.
• Experience with GCP technologies and container orchestration.

Responsibilities

• Design scalable micro-service architectures for generative AI.
• Develop CI/CD pipelines ensuring robust deployment processes.

Skills

DevOps

MLOps

Systems Engineering

Python

Bash

Docker

Kubernetes

CI/CD

Education

Bachelor's degree in Computer Science

Equivalent experience in Software Engineering

Tools

Google Cloud Platform

GitHub Actions

Circle CI

Jenkins

Job description

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About UnifyCX

At UnifyCX, we're redefining the nexus between technology and human-centric services. Our team, backed by founders with a history of successful IPOs, specializes in real-world AI solutions revolutionizing customer experience (CX). Our approach uniquely combines innovative AI with a human touch, empowering small to medium-sized businesses with scalable, efficient services.

About The Role

Unify is spearheading innovation in the generative AI space, focusing on enhancing customer experiences (CX) through cutting-edge technology.

As we expand our horizons, we're seeking a skilled Senior Platform Engineer to join our Platform Engineering team. This full-time role is tailor-made for a professional eager to dive deep into the complexities of scaling infrastructure, optimizing processes, and playing a pivotal role in our journey towards revolutionizing the CX domain. If you're passionate about MLOps, DevOps, and Systems Engineering, and looking to grow in these areas, you're the talent we're looking for.

What You Will Do

1. Design and Scale: Contribute to the design of scalable containerized micro-service architectures that support our generative AI technology, focusing on practical, efficient solutions.
2. Deploy and Manage: Utilize your expertise in Google Cloud Platform, Kubernetes, and CI/CD to deploy, manage, and scale our systems efficiently.
3. Automate and Optimize: Develop and maintain CI/CD pipelines using tools like Argo CD and Github Actions, ensuring seamless, reliable, and robust deployment processes.
4. Monitor and Operate: Create scalable monitoring solutions for all of Engineering to ensure our systems are healthy. Participate in 24x7 On-Call rotation, and lead testing and debugging efforts, maintaining high standards of quality and reliability.
5. Collaborate and Innovate: Work closely with cross-functional teams, including product managers and engineers, to craft and deliver high-quality, impactful solutions.
6. Code with Excellence: Write clean, efficient, and maintainable code, demonstrating best practices and coding standards to the broader Engineering team.

Requirements

• Educational Foundation: Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent experience.
• Proven Experience: 7+ years in DevOps roles with a focus on cloud infrastructure.
• Cloud Providers: Experience with Google Cloud Platform (GCP) technologies such as Cloud Storage, Compute Engine, GKE, Pub/Sub, or similar.
• Programming: Experience with Bash (Shell), Python, or Node.js.
• Container Orchestration: Deep knowledge of Docker and understanding of Kubernetes fundamentals.
• CI/CD: Experience with Github Actions, Circle CI, Jenkins, or similar tools; understanding of CI/CD in SDLC.
• Work Hours: Ability to work aligned with Eastern Standard Time (EST).

Preferred Qualifications

• Proficiency with other cloud platforms such as AWS and/or Azure.
• Experience building MLOps pipelines (e.g., MLflow, Kubeflow).
• Knowledge of data workflow and orchestration tools like Airflow, Dagster, or Prefect.
• Experience with generative AI models and platforms (e.g., OpenAI APIs, Hugging Face Transformers), including prompt engineering, fine-tuning, and deployment.