DevOps Integration Engineer

About the Company:

Clarifai is a leading, full-lifecycle deep-learning AI platform for computer vision, natural language processing, audio recognition, and large language models (LLM). We help organizations transform unstructured images, video, text, and audio data into structured data at a significantly faster and more accurate rate than humans would be able to do on their own. Founded in 2013 by Matt Zeiler, Ph.D. Clarifai has been a market leader in AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai continues to grow with employees remotely based throughout the United States, Canada, Argentina, India, and Estonia.

We have raised $100M in funding to date, with $60M coming from our most recent Series C. We are backed by industry leaders like Menlo Ventures, Union Square Ventures, Lux Capital, New Enterprise Associates, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm and Osage.

Your Impact:

• This position offers the opportunity to be a key player in deploying and integrating the Clarifai platform, directly engaging with customers. Your expertise will ensure seamless integration, effective issue resolution, and strong communication between customers and the Clarifai team, ultimately driving customer satisfaction and product success.

The Opportunity:

• Join the field engineering team as a core contributor for platform deployment and integration.
• Travel globally to directly engage with customers.
• Solve integration issues and provide hands-on support to customers.
• Collaborate closely with customers to ensure seamless integration and satisfaction.
• Act as a bridge between customers and the rest of the team, facilitating smooth communication and resolution of any challenges.

Requirements:

• Designing, implementing, and maintaining containerized workflows using Kubernetes, Docker, and Rancher.
• Hands-on experience (minimum 2 years) in deploying and managing containerized applications in production environments.
• Proficiency in Python programming and shell scripting in Linux environments.
• Experience with version control systems such as GitHub, with at least 2 years using Git or similar systems for collaborative software development.
• Knowledge of DOD systems and networking architectures, including familiarity with DOD standards, protocols, and security requirements.
• Demonstrated ability to effectively communicate with customers to understand their requirements.
• Proficiency in conveying technical concepts clearly to non-technical stakeholders.
• Experience in analyzing and incorporating customer feedback to enhance product offerings.
• Strong commitment to exceptional customer service and adaptability to changing requirements.
• Familiarity with DevOps methodologies and tools, including hands-on experience with continuous integration/continuous deployment (CI/CD) pipelines using tools like Jenkins, Ansible, or Terraform.
• Certifications such as Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD) are preferred.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including deploying, managing, or optimizing cloud-based infrastructure and services for scalable and secure applications.
• Preferably holds a certification as an AWS Certified Cloud Practitioner.
• Willingness to travel to customer sites, with approximately 30% travel expected based on customer needs.
• Minimum of 3 years of relevant experience in software integration, DevOps, or related fields.
• Possession of a Security Clearance of TS/SCI is mandatory.
• Proven track record of working on classified projects within government or defense sectors.
• Certification: Security Plus (Security+).

Nice to Have:

• Previous employment in government or defense contracting environments, with a strong understanding of regulatory environments and project requirements.
• Experience in rapid prototyping to quickly iterate and validate concepts, with at least 2 years of experience in rapidly developing prototypes or proof-of-concept solutions to test feasibility and gather feedback for iterative improvements.