The AI Engineer plays a critical role in advancing our mission to build more resilient, efficient, and intelligent systems that enhance data accessibility, integrity, and scalability. This role exists to develop and deploy AI agents, machine learning models, natural language processing tools, and intelligent automation solutions.

By leveraging AI technologies, the AI Engineer contributes to the creation of smarter protocols, predictive analytics, and adaptive systems that support our goals of innovation and customer focus. This position is instrumental in transforming complex data into actionable insights, enabling more intuitive user experiences, and driving innovation across the ecosystem.

Key Responsibilities:

General Innovation ideation and POCs
Design and Develop AI models and algorithms from scratch (test, deploy, and maintain AI systems)
Work on functional design, process design ( including scenario design, flow mapping), prototyping, testing, training, and defining support procedures.
Articulate and document the solutions architecture and lessons learned for each exploration and accelerated incubation.
Implement AI solutions that integrate with existing business systems to enhance functionality and user interaction.
Manage the data flow and infrastructure for effective AI deployment
Stay current with AI trends and suggest improvements to existing systems and workflows (conducting assessments of the AI and automation market and competitor landscape).
Collecting and analyzing large amounts of data/programming AI software to utilize large amounts of data
Collaborate with data scientists and other engineers to integrate AI into broader system architectures
Advise executives and business leaders on a broad range of technology, strategy, and policy issues associated with AI

Essential Knowledge and Experience:

Experience with innovation accelerators
Experience with cloud environments
MLOps tools: Experience using tools for lifecycle management of machine learning models.
Docker: Experience in using Docker to create reproducible and scalable environments.
Machine Learning Models: Advanced knowledge in Machine Learning models and Large Language Models (LLM), using langchain or a similar tool.
LLM Implementation: Experience in implementing LLMs using vector bases and Retrieval-Augmented Generation (RAG), as well as tuning models. Using GPTs, Llama, or any other LLM
Solution Architecture Validation: Ability to perform solution architecture validations for LLMs.
GenAIOps: Experience in putting Generative AI (GENAI) models into production and providing support to them.
Knowledge of basic algorithms, object-oriented and functional design principles, and best-practice patterns.
Experience in REST API development, NoSQL database design, and RDBMS design and optimizations.

Education:

Bachelors Degree or Graduate Degree
3-5 years of previous relevant experience

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