Digital & IT Senior Analyst - AI/ML Engineer

Position Summary Position Summary Our Digital Technology organization builds data- and AI-powered experiences for internal users and customers. The team spans data engineering, ML engineering, product, and platform operations, working end-to-end from data pipelines through model deployment, monitoring, and continuous improvement. The Senior AI / ML Engineer will lead the design, delivery, and operations of machine learning and generative AI solutions across our digital products and platforms. This senior role balances hands-on engineering, architectural leadership, and cross-functional collaboration to drive measurable business outcomes, while ensuring responsible AI practices and robust production reliability. This role reports to the Enterprise Digital and IT Lead and is recognized as a subject matter expert (SME) in AI solutions, enterprise integrations and modern software development practices, operate autonomously, set technical standards, mentor others, and influence AI strategy across multiple teams and domains Responsibilities Essential Functions: - Own AI initiatives from problem framing through deployment and monitoring (data, modeling, evaluation, serving, and iteration). - Design, train, and optimize models for NLP/LLM use cases (e.g., RAG pipelines, fine-tuning, prompt engineering, safety and guardrails). - Build reliable ML infrastructure and services (APIs, containers, Kubernetes), integrating CI/CD and automated testing. - Establish evaluation frameworks (offline metrics, online A/B tests, human-in-the-loop reviews) with clear success criteria. - Implement observability for models (drift detection, performance/SLOs, error analysis, data quality checks). - Ensure security, privacy, and compliance (PII handling, model safety, prompt-injection defenses, auditability). - Partner with product to scope roadmaps, estimate effort, and align technical plans with business outcomes. - Mentor engineers, contribute to architecture decisions, and champion best practices across the AI/ML stack. Qualifications Qualifications - 4-year University degree - Five or more years of experience in Information Technology - Programming: Expert in Python and SQL; strong software engineering practices (testing, patterns, performance). - Classical ML: supervised/unsupervised learning, model evaluation, feature engineering, time series. - Deep Learning: PyTorch or TensorFlow, transformers, CV/NLP pipelines. - Generative AI: LLMs, RAG, fine-tuning, prompt design, evaluation metrics and guardrails. - Agentic AI: Practical experience with concepts such as tool-calling, reasoning loops, task planning or multi-agent orchestration (e.g., AutoGen, LangChain Agents, LangGraph) - Data processing: Spark/Databricks or equivalent; batch and streaming (e.g., Kafka). - Storage: relational and NoSQL; data lakes; vector databases (e.g., FAISS, Pinecone, Weaviate). - CI/CD (e.g., GitHub Actions, GitLab CI), containerization (Docker), orchestration (Kubernetes). - Experiment tracking and model management (e.g., MLflow, Weights & Biases, DVC). - Cloud: Proficiency with one major cloud (AWS, GCP, or Azure) for training and serving (e.g., SageMaker, Vertex AI, AKS). - Security and Privacy: Experience handling sensitive data (PII), encryption, access controls, secure model serving. - Search and retrieval: Elastic/OpenSearch, knowledge graphs, advanced RAG patterns. - Ethics and Compliance: Champions responsible AI and governance. - Delivery: On-time, high-quality deployment of ML/LLM features into production. - Assess current AI/ML assets, data pipelines, and platform maturity; identify quick wins and strategic gaps. Equal Employment Opportun