Senior AI​/Machine Learning Engineer; Public Trust

Location: Bethesda, MD (Hybrid)

Clearance: Public Trust (Role may require additional background investigations)

Employment Type: Full-Time

US Citizenship is Required

Praescient Analytics is seeking a highly experienced Senior AI/ML Engineer to support a mission-critical federal analytics and artificial intelligence modernization program. This role is responsible for designing, engineering, deploying, and operating production‑grade AI and machine learning systems that support real‑time decision-making, advanced analytics, and public safety outcomes.

The Senior AI/ML Engineer owns the end‑to‑end machine learning lifecycle
, from data ingestion and model development through deployment, monitoring, and governance. This position plays a key role in operationalizing advanced analytics, building scalable MLOps pipelines
, and integrating AI capabilities into cloud‑native systems within a highly regulated federal environment.

• Design, develop, train, and optimize machine learning and deep learning models supporting predictive analytics, classification, anomaly detection, NLP, and AI‑assisted decision support.
• Apply advanced ML techniques using supervised, unsupervised, and semi‑supervised learning approaches.
• Design and implement end‑to‑end MLOps pipelines supporting model training, validation, versioning, deployment, and rollback.
• Deploy models into production environments using Microsoft Azure ML
  , containerization, and CI/CD pipelines.
• Implement model monitoring, performance tracking, and drift detection to ensure long‑term reliability and accuracy.
• Build and maintain AI solutions leveraging Microsoft Azure services
  , including:
  • Azure Machine Learning
  • Azure Data Lake Storage
  • Azure Synapse Analytics
• Integrate AI/ML solutions with existing data platforms, APIs, and analytics services.
• Optimize AI workloads for cost, performance, and reliability in cloud environments.
• Ensure AI systems adhere to federal standards for responsible AI
  , transparency, explainability, and auditability.
• Support model documentation, validation artifacts, and compliance reporting.
• Collaborate with governance and stakeholder teams to ensure models are suitable for high‑impact decision‑making.
• Work closely with data scientists, data engineers, analysts, architects, and Agile project managers in a fast‑paced Agile delivery environment.
• Mentor junior engineers and contribute to shared engineering standards and best practices.

• Minimum of 5+ years of experience designing, deploying, and operating production AI/ML systems.
• Demonstrated experience moving models from research or prototype stages into production environments.
• Proven experience supporting AI systems in mission‑critical or regulated settings.
• Bachelor's Degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field — or equivalent professional experience.
• Experience supporting federal, public‑sector, or other regulated environments
  .

• Expert proficiency in Python and AI/ML frameworks such as Py Torch and/or Tensor Flow
  .
• Strong experience with machine learning libraries including Scikit‑learn, Num Py, and Pandas
  .
• Hands‑on experience with Microsoft Azure AI/ML services
  , particularly Azure Machine Learning.
• Experience building CI/CD pipelines for ML workflows.
• Familiarity with containerization (Docker) and model deployment patterns.
• Experience with SQL and data integration for ML workloads.
• Proficiency with Git‑based version control and collaborative development.

• Strong written and verbal communication skills.
• Ability to explain complex AI/ML concepts to technical and non‑technical stakeholders.
• Proven ability to lead technical efforts and collaborate across multidisciplinary teams.

• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Engineering, or a related field — or equivalent professional experience.
• Familiarity with AI agents, vector databases, large language models (LLMs), or Copilot‑style architectures
  .
• Azure certifications (AI Engineer, Data Scientist, or related).
• Experience with streaming or near‑real‑time AI pipelines.