AI/ML Engineer
Actively Hiring
Full-time Posted 5 months ago
Responsibilities
- check_circle Build & Ship Production Models
- check_circle Implement and productionize ML solutions (supervised/unsupervised, NLP, deep learning) with robust data preprocessing, feature engineering, and evaluation pipelines.
- check_circle Support model selection, training, validation, optimization, and calibration, ensuring reliability, fairness, and performance at scale.
- check_circle Own the MLOps Lifecycle (Azure)
- check_circle Establish MLOps workflows (CI/CD for ML, experiment tracking, model registry, reproducible builds and deployments).
- check_circle Implement model monitoring (drift, data/feature quality, bias, and business KPIs), alerting, and automated rollback to keep systems safe and responsive.
- check_circle Data Engineering for ML
- check_circle Design high-quality data pipelines (ingest, transform, validate) across structured and unstructured sources; enforce data contracts and lineage.
- check_circle Partner with analytics teams to make datasets discoverable, documented, and performant for iterative model development.
- check_circle AI Agents & Copilot Integration
- check_circle Build AI agents that operationalize safety analytics (Copilot Studio, Python agents, retrieval pipelines) to accelerate triage and decision flow.
- check_circle Integrate agents with APIs, event streams, dashboards, and case management systems to reduce cycle time from signal to action.
- check_circle Engineering Excellence & Governance
- check_circle Champion secure-by-design practices, reproducibility, and auditability (model cards, data sheets, deployment records).
- check_circle Contribute to coding standards, code reviews, and knowledge sharing; mentor engineers and data scientists.
- check_circle Agile Collaboration & Impact
- check_circle Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement.
- check_circle Translate mission goals into technical roadmaps and measurable outcomes tied to Sentinel time-to-intervention targets.
Basic 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
Fulltime Ai Ai Engineer Machine Learning Deep Learning Mlops