Role: Mid Level MLOps (Machine Learning Engineer)
Location: Fully Remote
Duration: Full Time
As a Mid-Level AI/ML Engineer you will support the design, deployment, and operation of production-grade AI systems. Sentinel model — protecting more consumers, faster, from more hazards by using analytics to shorten time to intervention. You will contribute to end-to-end model lifecycle engineering on Azure, advance MLOps best practices, and help build AI agents using Copilot Studio and Python frameworks that translate safety 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.
MLOps Lifecycle (Azure)
Establish MLOps workflows including CI/CD for ML, experiment tracking, model registry, and reproducible builds and deployments.
Implement model monitoring (drift, data/feature quality, bias, and business KPIs), alerting, and automated rollback.
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.
Engineering Excellence & Governance
Champion secure-by-design practices, reproducibility, and auditability including model cards, data sheets, and deployment records.
Contribute to coding standards and code reviews; support knowledge sharing across the team.
Agile Collaboration & Impact
Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement.
Translate mission goals into technical contributions aligned with Sentinel time-to-intervention targets.
Required Qualifications
Experience: 3+ years hands-on developing and deploying AI/ML models in production environments.
Programming: Proficient in Python, including packaging, testing, and 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 including deployment, monitoring, scaling, CI/CD, experiment tracking, and model registry.
Cloud: Experience with Azure for AI/ML workloads, including Azure ML, Azure Synapse, and Azure Data Lake.
AI Agents: Experience developing AI agents in Copilot Studio and via Python frameworks.
Education: Bachelor''''''''s degree or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related field.
Must have: Python coding, hands-on end to end ML pipelines, strong in Azure services use for data ingestion/storage/model deployment (but AWS or Google Cloud Platform can be subbed if strong)
Nice to have: Infrastructure as code (e.g. Terraform or comparable)
Preferred Qualifications
Experience with streaming/event-driven architectures (Event Hubs), feature stores, and vector databases for retrieval augmented generation (RAG).
Hands-on with responsible AI including fairness, explainability, privacy, model governance, and security in cloud ML.
Familiarity with domain-specific risk analytics and public sector or regulated environments.
Certifications in Azure AI/ML and/or MLOps.
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