Agile Programming Methodologies, Algorithms, Analysis Skills, Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Background Investigation, Best Practices, Calibration, Case Management, Channel Strategies, Cloud Computing, Code Reviews, Coding Standards, Communication Skills, Computer Science, Computer Skills, Consulting, Consumer Protection, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Data Lake, Data Management, Data Modeling, Data Quality, Data Science, Data Visualization, Datasheets, Decision Support, Deep Learning, Detail Oriented, Federal Government, Government, Hubs, Machine Tool, Mathematics, Mentoring, Microsoft Windows Azure, Natural Language Processing (NLP), Organizational Skills, Performance Metrics, Performance Testing, Performance Tuning/Optimization, Problem Solving Skills, Public Safety, Python Programming/Scripting Language, Reporting Dashboards, Risk Analysis, Security Auditing, Statistics, Team Player, Time Management, Training Data Sets
Role: Senior AI/ML Engineer
Location: Fully Remote
Duration: Full Time
Job Description
As a Senior AI/ML Engineer , you will lead 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 own end-to-end model lifecycle engineering on Azure, advance MLOps best practices, and build AI agents using Copilot Studio and Python frameworks that translate signals into timely, actionable decisions. This role offers a high-impact opportunity to apply advanced AI/ML expertise in a federal mission environment, directly improving public safety outcomes.
Key Responsibilities
Build & Ship Production Models
Architect, implement, and productionize ML solutions (supervised/unsupervised, NLP, deep learning) with robust data preprocessing, feature engineering, and evaluation pipelines.
Lead model selection, training, validation, optimization, and calibration, ensuring reliability, fairness, and performance at scale.
Own the 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 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 including model cards, data sheets, and 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: 5+ years hands-on developing and deploying AI/ML models in productionenvironments.
Programming: Expert in Python, including packaging, testing, and performance optimization.
ML Expertise: Deep understanding of algorithms, model selection,training/validation/optimization, and evaluation at scale.
Data Skills: Expert in data preprocessing, feature engineering, and data visualization for decision support.
Deep Learning & MLOps: Expert with PyTorch/TensorFlow and modern MLOps including deployment, monitoring, scaling, CI/CD, experiment tracking, and model registry.
Cloud: Proven 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 frameworksincluding tooling, orchestration, retrieval, and connectors.
Education: PhD, Master's degree, or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related field.
Preferred Qualifications
Experience with streaming/event-driven architectures (Event Hubs), feature stores, andvector databases for retrieval augmented generation (RAG).
Hands-on with responsible AI including fairness, explainability, privacy, model governance (model cards, audits), 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.
Other Qualifications
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 diversetechnical and non-technical audiences.
Highly organized, detail-oriented, and proactive; capable of working independently with minimal supervision.
Must be able to pass a federal agency background check and obtain a government-issued ID badge prior to starting work.
Public sector consulting experience is a plus.