AI​/ML Engineer- Mid-Level

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.

Implement and product ionize 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.

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.

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.

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.

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.

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.

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/Tensor Flow 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.

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.

Strong analytical and problem-solving skills, with the ability to break down complex processes and design effective solutions.

Excellent communication skills, able to translate complex technical concepts for diverse audiences.

Demonstrated eagerness to learn and adopt new technologies, while actively sharing knowledge with the team.

Must be able to pass a federal agency background check and obtain a government-issued  prior to starting work.

Public sector consulting experience is a plus.