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Job Title: DevOps Specialist & Data Engineer

Location: Remote

Type: Full-time

Experience Level: Senior

Industry: Generative AI / Artificial Intelligence / Machine Learning

Reports To: Head of Engineering / CTO

Job Title: DevOps Specialist & Data Engineer

Location: Remote

Type: Full-time

Experience Level: Senior

Industry: Generative AI / Artificial Intelligence / Machine Learning

Reports To: Head of Engineering / CTO

Ready to join a cutting edge AI company? We’re on a mission to become the OpenAI of the spicy content industry, building a full-spectrum ecosystem of revolutionary AI infrastructure and products. Our platform, OhChat, features digital twins of real-world personalities and original AI characters, enabling users to interact with lifelike AI-generated characters through text, voice, and images, with a roadmap that includes agentic superModels, API integrations, and video capabilities.

We are looking for a Senior DevOps Specialist with a strong python and data engineering background to support our R&D and tech teams by designing, building, and maintaining robust infrastructure and data pipelines across AWS and GCP. You will be instrumental in ensuring our systems are scalable, observable, cost-effective, and secure. This role is hands-on, cross-functional, and central to our product and research success.

• Design, implement, and maintain infrastructure on AWS and Google Cloud Platform (GCP) to support high-performance computing workloads and scalable services.

• Collaborate with R&D teams to provision and manage compute environments for model training and experimentation.

• Maintain / monitor systems, implement observability solutions (e.g., logging, metrics, tracing), and proactively resolve infrastructure issues.

• Manage CI/CD pipelines for rapid, reliable deployment of services and models.

• Ensure high availability, disaster recovery, and robust security practices across environments.

• Build and maintain data processing pipelines for model training, experimentation, and analytics.

• Work closely with machine learning engineers and researchers to understand data requirements and workflows.

• Design and implement solutions for data ingestion, transformation, and storage using tools such as Scrappy, Playwright, agentic workflows (e.g. crawl4ai) or equivalent.

• Optimize and benchmark AI training / inference / data workflows to ensure high performance, scalability, cost and an exceptional customer experience.

• Maintain data quality, lineage, and compliance across multiple environments.

• 5+ years of experience in DevOps, Site Reliability Engineering, or Data Engineering roles.

• Deep expertise with AWS and GCP, including services like EC2, S3, Lambda, IAM, GKE, BigQuery, and more.

• Strong proficiency in infrastructure-as-code tools (e.g., Terraform, Pulumi, CloudFormation).

• Extensive hands-on experience with Docker, Kubernetes, and CI/CD tools such as GitHub Actions, Bitbucket Pipelines, or Jenkins, with a strong ability to optimize CI/CD workflows as well as AI training and inference pipelines for performance and reliability."

• Exceptional programming skills in Python. You are expected to write clean, efficient, and production-ready code. You should be highly proficient with modern Python programming paradigms and tooling.

• Proficiency in data-centric programming and scripting languages (e.g., Python, SQL, Bash).

• Proven experience designing and maintaining scalable ETL/ELT pipelines.

• Focused, sharp, and results-oriented: You are decisive, work with a high degree of autonomy, and consistently deliver high-quality results. You are quick to understand and solve the core of a problem and know how to summarize it efficiently for stakeholders.

• Effective communicator and concise in reporting: You should be able to communicate technical insights in a clear and actionable manner, both verbally and in written form. Your reports should be precise, insightful, and aligned with business objectives.

• Experience supporting AI/ML model training infrastructure (e.g., GPU orchestration, model serving) for both Diffusion- and LLM pipelines.

• Familiarity with data lake architectures and tools like Delta Lake, LakeFS, or Databricks.

• Knowledge of security and compliance best practices (e.g., SOC2, ISO 27001).

• Exposure to MLOps platforms or frameworks (e.g., MLflow, Kubeflow, Vertex AI).

• Competitive salary + equity

• Flexible work environment and remote-friendly culture

• Opportunities to work on cutting-edge AI/ML technology

• Fast-paced environment with high impact and visibility

• Professional growth support and resources