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Known enterprise, mid-level generalist ML role and metro hybrid increases applicant competition.
ML/AI and cloud engineering skills are transferable but AI services domain adds moderate industry specificity.
Multiple mandatory cloud, language, microservices and data skills make screening moderately strict.
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Support design, development, and testing of software systems, focusing on cloud-based modules and micro-services.
Develop and prototype multi-vendor infrastructure components incorporating ML/AI algorithms on structured and unstructured data.
Collaborate in code development, automated testing, deployment, and documentation for distributed systems under various stakeholder instructions.
Bachelor's degree or equivalent in Computer Science, Engineering, or related field.
Moderate experience in software development including micro-services, RESTful APIs, and full software delivery lifecycle tools.
Familiarity with cloud architectures (AWS, GCP, Azure) and programming languages such as C++, Java, Python, Node.js.
Workplace type: Hybrid; targeted experience working with geo-distributed teams through various product phases.
Developing subject matter expert comfortable working within cloud architectures and container runtimes like Kubernetes and Docker.
Experienced in Agile and Lean methodologies with hands-on exposure to DevOps, CI/CD, and automated test-driven development.
Capable of handling large datasets and applying ML/AI algorithms, with strong problem-solving and debugging skills in a dynamic team environment.