GDIT's Intelligence and Homeland Security (IHS) CTO organization needs to drive technical solutions for complex and strategic deals, requiring a highly technical AI/ML Delivery Engineer to bridge the gap between data science, software engineering, and systems architecture for robust, scalable, and fit-for-mission AI solutions.
Requirements
- Expert-level Python proficiency; strong familiarity with C++, Go, or Java for integration and performance-critical workloads.
- TensorFlow, PyTorch, JAX, ONNX, Hugging Face Transformers, scikit-learn.
- NumPy, pandas, SciPy, scikit-learn, LangChain, R, SQL
- MLflow, Airflow, Kubeflow, DVC, BentoML, Weights & Biases.
- Spark, Databricks, Kafka, Delta Lake, Snowflake, or BigQuery.
- AWS (SageMaker, Bedrock), Azure (Machine Learning, Synapse), or GCP (Vertex AI, Dataflow).
- Docker, Kubernetes, Terraform, Helm, GPU/TPU orchestration.
Responsibilities
- Architect end-to-end AI/ML systems, from data ingestion pipelines and feature stores to model training, evaluation, and deployment in production environments.
- Design distributed and scalable ML workflows leveraging cloud-native technologies (e.g., Kubernetes, Kubeflow, MLflow, SageMaker, Vertex AI, Azure ML, Nvidia ecosystem).
- Integrate MLOps principles, including CI/CD for ML, model versioning, and automated retraining pipelines.
- Ensure model governance, data lineage, and compliance with US Federal and State requirements.
- Collaborate with data scientists to develop and optimize models for computer vision, NLP, predictive analytics, and generative AI.
- Implement advanced model deployment strategies (e.g., ensemble serving, A/B testing, online learning) to include agile AI/ML Model Deployment Operations (ModelOps).
- Design APIs and microservices for AI model consumption across applications and external systems.
Other
- 10+ years of professional experience in data science and AI/ML engineering and/or Data Science.
- Bachelor of Science in Computer Science, Information Technology, similar discipline or equivalent experience.
- Experience with contributing to Federal solicitation responses.
- Experience working with large data sets including data integration, data migration, analysis and visualization
- Experience with cloud-native data analytic solution architectures
