The AI/ML Engineer will support the design, deployment, and operation of production-grade AI systems that power CPSC's Sentinel model - protecting more consumers, faster, from more hazards by using analytics to shorten time to intervention. This role supports end to end model lifecycle engineering on Azure, advance MLOps best practices, and build AI agents (Copilot Studio + Python frameworks) that translate signals into timely, actionable decisions.
Key Responsibilities
- Build & Ship Production Models
- Implement and productionize ML solutions (supervised/unsupervised, NLP, deep learning) with robust data preprocessing, feature engineering, and evaluation pipelines.
- Support model selection, training, validation, optimization, and calibration, ensuring reliability, fairness, and performance at scale.
- Own the MLOps Lifecycle (Azure)
- Establish MLOps workflows (CI/CD for ML, experiment tracking, model registry, reproducible builds and deployments).
- Implement model monitoring (drift, data/feature quality, bias, and business KPIs), alerting, and automated rollback to keep systems safe and responsive.
- Data Engineering for ML
- Design high-quality data pipelines (ingest, transform, validate) across structured and unstructured sources; enforce data contracts and lineage.
- Partner with analytics teams to make datasets discoverable, documented, and performant for iterative model development.
- AI Agents & Copilot Integration
- Build AI agents that operationalize safety analytics (Copilot Studio, Python agents, retrieval pipelines) to accelerate triage and decision flow.
- Integrate agents with APIs, event streams, dashboards, and case management systems to reduce cycle time from signal to action.
- Engineering Excellence & Governance
- Champion secure-by-design practices, reproducibility, and auditability (model cards, data sheets, deployment records).
- Contribute to coding standards, code reviews, and knowledge sharing; mentor engineers and data scientists.
- Agile Collaboration & Impact
- Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement.
- Translate mission goals into technical roadmaps and measurable outcomes tied to Sentinel time-to-intervention targets.
Required Qualifications
- Experience: 3+ years handson developing and deploying AI/ML models in production environments.
- Programming: Proficient in Python (including packaging, testing, performance optimization).
- ML Expertise: Understanding of algorithms, model selection, training/validation/optimization, and evaluation at scale.
- Data Skills: Proficient in data preprocessing, feature engineering, and data visualization for decision support.
- Deep Learning & MLOps: Proficient with PyTorch/TensorFlow, and modern MLOps (deployment, monitoring, scaling, CI/CD, experiment tracking, model registry).
- Cloud: Experience with Azure for AI/ML workloads (e.g., Azure ML, Azure Synapse, Azure Data Lake).
- AI Agents: Experience developing AI agents in Copilot Studio and via Python frameworks (tooling, orchestration, retrieval, connectors).
- Bachelor's degree, or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, Engineering, related field, OR equivalent professional experience.
Preferred Qualifications
- Experience with streaming/event-driven architectures (Event Hubs), feature stores, and vector databases (for retrieval augmented generation).
- Hands-on with responsible AI (fairness, explainability, privacy), model governance (model cards, audits), and security in cloud ML.
- Familiarity with domain-specific risk analytics and public sector/regulated environments.
- Certifications in Azure AI/ML and/or MLOps advantageous.
Tags & focus areas
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