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.
• Bachelor’s 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.