Descripción de la oferta
Descripción del trabajo
The AI Engineer is a hands-on, early-career role responsible for designing, developing, and deploying AI solutions, including Machine Learning, Deep Learning, and Generative AI applications, that address real business challenges and deliver measurable value. Working within the Data & Digital Innovation team, the AI Engineer builds and optimizes AI applications using LLMs, RAG architectures, agents, and predictive models, and engineers the surrounding software (micro-services, APIs, and data integrations) required to bring them into production. The role combines solid software engineering fundamentals with applied AI expertise, collaborating closely with senior engineers, architects, and business stakeholders to turn prototypes into robust, scalable, enterprise-grade solutions, while continuously evaluating and adopting emerging AI technologies and best practices.
Antes de solicitar este puesto, por favor, lea la siguiente información sobre esta oportunidad que encontrará a continuación.
Key Accountabilities
Design, develop, and deploy AI solutions, including Machine Learning, Deep Learning, and Generative AI applications.
Build and optimize AI applications using LLMs, RAG architectures, agents, and predictive models.
Design and deploy applications with a micro-services architecture that integrate with enterprise systems, APIs, and databases.
Write high-quality, scalable, and maintainable code, contributing across all phases of the software development lifecycle.
Transition proofs of concept and prototypes into production-ready solutions, with a focus on performance, reliability, and security.
Evaluate and adopt new AI technologies, frameworks, and best practices, incorporating them into solution design where appropriate.
Apply modern engineering and DevOps practices (version control, CI/CD, and containerization) to ensure robust, repeatable deployments.
Collaborate with senior engineers, architects, and business stakeholders to align solutions with requirements and enterprise architecture standards.
Networking/Key relationships
Works closely with AI/ML leads and senior engineers within the Data & Digital Innovation team for technical guidance, code reviews, and mentoring.
Collaborates with data scientists and data analysts to operationalize models and deliver data-driven products.
Engages with enterprise and solution architects to ensure solutions align with enterprise architecture standards.
Partners with IT, SAP, and platform teams to integrate AI applications with enterprise systems, APIs, and databases.
Coordinates with cybersecurity architects to ensure solutions meet security and compliance requirements.
Interacts with business stakeholders to gather requirements, validate outcomes, and demonstrate business value.
Minimum Knowledge & Experience required for the position
Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, or a related technical field.
2–3 years of experience designing, building, and deploying AI/ML or software solutions in a professional environment.
Solid proficiency in Python and AI frameworks such as TensorFlow, PyTorch, and scikit-learn.
Working knowledge of AI, Generative AI, and prompt engineering fundamentals, including LLMs, RAG architectures, agents, and predictive models.
Hands-on experience with AWS cloud services such as S3, Lambda, EC2, Bedrock, and SageMaker.
Experience with core software engineering tools and practices, including Git and GitHub Actions, Docker, Kubernetes, and REST APIs.
Management has the discretion of substituting relevant work experience for a degree and/or making exceptions to the years of experience requirement.
Skills & Capabilities
Strong programming foundations and clean-code discipline, particularly in Python.
Applied understanding of machine learning, deep learning, and generative AI techniques.
Ability to design micro-services and integrate solutions with enterprise systems, APIs, and databases.
Curiosity and a research mindset, with eagerness to experiment with and learn emerging methods.
Solid problem-solving, analytical, and debugging skills.
Effective collaboration and communication across technical and non-technical audiences.
Ability to prioritize and execute in fast-moving, ambiguous contexts.
Strong commitment to quality, reliability, and security in delivered solutions.
Travel requirements
Travel is expected up to 10% of the time to support collaboration, team engagement, and stakeholder meetings.
Note
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