AI Engineer

Mandatory Skills & Experience
A passionate developer with 3+ years of experience in Java, Python, and Kubernetes, comfortable working as part of a paired/balanced team.
Foundational experience in software development, with exposure to AI/ML technologies.
Proficiency in GenAI frameworks: Experienced in using GenAI frameworks and libraries such as OpenAI API.
Prompt engineering: Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance.
Experience developing solutions that leverage cloud-native technologiesfeaturing container[1]based, microservices-based approaches; based on applying 12-factor principles to application engineering.
Strong verbal and written communication skills (English).
Positive and solution-oriented mindset.
Experience delivering Agile and Scrum projects in a Jira-based project management environment.

Desired Skills & Experience
Understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models.
AI ethics and bias mitigation: Knowledgeable about ethical considerations in AI and experienced in implementing strategies to mitigate bias in AI models.
Knowledgeable about vector databases, LLMs, and integrating with such models.
Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e.g., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE).
Understanding of core practices including DevOps, SRE, Agile, Scrum, Domain-Driven Design, and familiarity with the CNCF open-source community.
Recognized with cloud and technical certifications, ideally including AI/ML specializations from providers like Google, Microsoft, AWS, Linux Foundation, IBM, or Red Hat.

Verifiable Certification
At least one recognized cloud professional / developer certification (AWS/Google/Microsoft)