← Serch more jobs

AI/ML Engineer

LinkedIn General Dynamics Information Technology Washington, DC
Not Applicable Posted April 4, 2026 Job link
Thinking about this job
Not Met Priorities
What still needs stronger evidence
Requirements
  • Clearance Level Must Currently Possess:
  • None
  • Clearance Level Must Be Able To Obtain:
  • None
  • Public Trust/Other Required:
  • Data Science and Data Engineering
  • Cascading Style Sheets (CSS), Data Science, Machine Learning (ML), Machine Learning Algorithms
  • 5 + years of related experience
  • US Citizenship Required:
  • Bachelor’s degree in relevant field and 5+ years of experience
  • Analytical & Programming
  • Strong Python (data manipulation, model development; libraries like Pandas, NumPy, scikit-learn).
  • SQL proficiency (joins, window functions, performance-aware queries).
  • Statistical foundations (probability, hypothesis testing, regression, experimental design/A-B testing).
  • Data Modeling
  • End-to-end ML workflow experience (feature engineering, training, validation, deployment, monitoring).
  • Data wrangling & ETL/ELT (building reliable pipelines; handling messy, large datasets).
  • Model evaluation (metrics selection, bias/variance trade-offs, error analysis).
  • AI Integration w/ MLOps
  • Hands-on API integration for AI services (e.g., calling model endpoints, building microservices).
  • Production deployment of models (packaging, versioning, CI/CD for ML).
  • Model monitoring (drift detection, performance tracking, retraining triggers).
  • Cloud Platforms
  • Experience with at least one major cloud (Azure, AWS, or GCP) for data/AI workloads.
  • Familiarity with containers (Docker) and source control (Git).
  • Data visualization skills (Power BI or Tableau) to communicate insights and outcomes.
  • Communication
  • System analysis skills to identify viable AI insertion points in processes, products, or workflows.
  • Stakeholder communication (translating technical findings into business value and concrete recommendations).
  • Documentation of models, assumptions, data lineage, and decisions.
  • Basic understanding of data security and access controls in production environments.
  • Tableau/Power BI advanced (parameterized dashboards, Row-Level Security).
  • Clearance: Candidates must be eligible to obtain a federal security clearance
Preferred Skills
  • Production deployment of models (packaging, versioning, CI/CD for ML).
  • Model monitoring (drift detection, performance tracking, retraining triggers).
  • Experience with at least one major cloud (Azure, AWS, or GCP) for data/AI workloads.
  • Familiarity with containers (Docker) and source control (Git).
  • Data visualization skills (Power BI or Tableau) to communicate insights and outcomes.
  • System analysis skills to identify viable AI insertion points in processes, products, or workflows.
  • Documentation of models, assumptions, data lineage, and decisions.
  • Governance/Security
  • Responsible AI awareness (fairness, explainability, privacy, and compliance considerations).
  • Basic understanding of data security and access controls in production environments.
  • Advanced AI/LLM
  • Experience with LLMs (e.g., Azure OpenAI Service/OpenAI API) for summarization, classification, or copilots.
  • Prompt engineering and evaluation of LLM outputs for quality and safety.
  • RAG pipelines (retrieval-augmented generation), vector databases (e.g., Azure AI Search, Pinecone, FAISS), and embeddings.
  • Fine-tuning or model adaptation strategies for domain-specific use cases.
  • MLOps Engineering
  • Model orchestration/experiment tracking (MLflow, Weights & Biases).
  • Kubernetes and ML deployment tools (e.g., AKS/EKS, Argo, KServe).
  • Feature stores, A/B testing frameworks, and event-driven/streaming data (Kafka, Kinesis).
  • CI/CD pipelines (GitHub Actions, Azure DevOps) and Infrastructure as Code (Terraform, Bicep).
  • Data Platform Integration
  • Databricks, Snowflake, or BigQuery experience.
  • Building robust APIs (REST/GraphQL) and microservices around models.
  • Monitoring & Observability (Prometheus, Grafana; app & model logs).
  • Responsible AI & Compliance
  • Practical experience with model risk management, documentation standards, and explainability (SHAP, LIME).
  • Knowledge of privacy-by-design and PII handling (data minimization, anonymization).
  • (If applicable to the environment) familiarity with FedRAMP or regulated environments.
  • Additional Languages/Tools
  • R, PySpark, or Scala for data-intensive workloads.
  • LangChain or Semantic Kernel for LLM app development.
  • Tableau/Power BI advanced (parameterized dashboards, Row-Level Security).
Education
  • (Not required) – None
  • (Not required) – Bachelor’s degree in relevant field and 5+ years of experience
  • (Not required) – Advanced AI/LLM
  • (Not required) – Experience with LLMs (e.g., Azure OpenAI Service/OpenAI API) for summarization, classification, or copilots.
  • (Not required) – MLOps Engineering
  • (Not required) – Kubernetes and ML deployment tools (e.g., AKS/EKS, Argo, KServe).
  • (Not required) – Feature stores, A/B testing frameworks, and event-driven/streaming data (Kafka, Kinesis).
  • (Not required) – CI/CD pipelines (GitHub Actions, Azure DevOps) and Infrastructure as Code (Terraform, Bicep).
  • (Not required) – Data Platform Integration
  • (Not required) – Databricks, Snowflake, or BigQuery experience.