AI Engineer Global IT Infrastructure

Role Summary
The AI Engineer contributes to the end-to-end delivery of AI/ML solutions within the Office of Innovation working alongside broader platform teams. This role spans model development support GenAI application building infrastructure automation and governance tooling making it an ideal position for an engineer ready to deepen expertise across the full AI delivery stack. The successful candidate is technically sharp eager to grow and comfortable operating in a fast-paced innovation environment within a global enterprise.

Key Responsibilities

AI/ML Model Development
Build train and evaluate ML models contributing to the full model lifecycle from data preparation through to deployment.
Conduct exploratory data analysis feature engineering and model experimentation; document findings clearly.
Support model validation testing and performance benchmarking activities.
Generative AI & LLM Applications
Develop and maintain GenAI-powered applications including chatbots summarization tools document processing pipelines and internal copilots.
Implement prompt engineering patterns retrieval-augmented generation (RAG) pipelines and tool-augmented agents using established frameworks.
Participate in the evaluation of new LLM capabilities and contribute to internal proof-of-concepts.

AI Infrastructure & MLOps
Support the building and maintenance of ML pipelines including data ingestion preprocessing training automation and model serving.
Manage and monitor deployed models; identify and escalate performance degradation drift or anomalies.
Contribute to infrastructure-as-code for AI workloads across cloud environments (AWS Azure or GCP).
AI Governance & Quality
Assist in producing governance documentation: model cards data lineage records and risk assessment inputs.
Implement monitoring and logging frameworks to support auditability and compliance requirements.
Apply responsible AI checklists and flag potential bias fairness or privacy concerns during development.

Collaboration & Delivery
Work closely with data engineers platform engineers and business analysts to integrate AI outputs into existing systems.
Participate in Agile ceremonies sprint planning and technical design discussions.
Write clean well-documented testable code and maintain internal technical documentation.

Qualifications
Essential
3 to 5 years of software engineering experience with at least 1 or 2 years in ML or AI-focused roles.
Solid skills with hands-on experience in ML libraries (scikit-learn PyTorch or TensorFlow pandas NumPy).
Working knowledge of LLM APIs (OpenAI Anthropic HuggingFace) and at least one orchestration framework (LangChain LlamaIndex).
Familiarity with cloud platforms (AWS Azure or GCP) and containerization basics (Docker).
Experience with version control (Git) basic CI/CD practices and collaborative development workflows.
Understanding of core ML concepts: supervised/unsupervised learning model evaluation overfitting and feature engineering.
Bachelors degree in computer science Engineering Mathematics or a related field or equivalent demonstrable experience.

Desirable
Exposure to MLOps tools such as MLflow DVC or Weights & Biases.
Experience with vector databases (Pinecone Weaviate ChromaDB pgvector) for semantic search or RAG applications.
Familiarity with responsible AI principles bias detection methods or model explainability tools.
Cloud certifications (AWS Azure or GCP) at associate level or above.
Experience in an enterprise IT environment (ITSM infrastructure networking) is a plus.