Artificial Intelligence/Machine Learning SME
Scientific Research Corporation4 months ago
Augusta, GA, USAStaff+
Responsibilities
- Serve as the primary AI/ML Engineering subject matter expert for AIDETL interns and staff.
- Provide guidance on AI strategy, architecture, policy, implementation, and best practices.
- Deliver expert-level AI/ML training, workshops, and briefings and assist with curriculum development.
- Build and maintain data pipelines, feature pipelines, model-training workflows, and production model-serving systems.
- Train and tune algorithms, including deep-learning models, to improve predictive accuracy and decision-support capabilities.
- Build end-to-end MLOps pipelines with model versioning, monitoring, automated testing, model validation, drift detection, data-quality checks, and audit trails.
- Deploy and operate models in development and production environments while ensuring reliability, performance, scalability, and compliance.
- Identify and integrate commercial off-the-shelf, government, and custom tools within established frameworks.
- Collaborate with cross-functional teams to align solutions with enterprise architecture, cybersecurity, and security requirements.
- Analyze system performance metrics and recommend improvements in efficiency and scalability.
- Mentor interns and junior engineers, lead cross-functional AI projects, and communicate technical concepts to technical and non-technical audiences.
Requirements
- Secret clearance and U.S. citizenship are required.
- A bachelor's degree in computer science, data science, artificial intelligence, engineering, or a related technical discipline plus 12–15 years of relevant experience, or a related master's degree plus 10–13 years of relevant experience.
- At least 5 years designing, building, and operating end-to-end AI/ML infrastructure in production environments.
- Expert-level proficiency with at least two of TensorFlow, PyTorch, JAX, or Scikit-Learn, including deep-learning architectures such as CNNs, RNNs, and Transformers and explainability methods such as SHAP, LIME, and counterfactuals.
- Experience building MLOps pipelines and deploying models using tools such as Kubeflow, MLflow, or TFX or similar technologies.
- Experience with pipeline instrumentation, monitoring, model-drift detection, data-quality checks, and compliance or audit trails.
- Strong programming skills in Python, R, or Java.
- Experience translating business problems such as computer vision, NLP, predictive maintenance, and anomaly detection into production ML solutions and evaluating them against domain-specific KPIs.
- Hands-on experience with generative AI, prompt engineering, chain-of-thought reasoning, and NLP tasks including entity extraction, summarization, and semantic search.
- Experience mentoring junior engineers, leading cross-functional AI projects, and presenting technical concepts to executives, product owners, and compliance teams.
- Familiarity with virtualized and containerized deployment environments such as VMware, Docker, and Kubernetes.
- Strong project management, documentation, presentation, communication, problem-solving, and analytical skills.
- Preferred qualifications include 12 or more years in AI/ML or data engineering, Agile Scrum team leadership, multi-enclave DoD experience, automated model validation and bias detection, enterprise API and microservices integration, GPU optimization, enterprise analytics or AI modernization, relevant publications or open-source contributions, AI-agent evaluation and observability, AI service integration, GPU programming, LLMs, and agent frameworks.
Benefits
- Medical, dental, and vision insurance plans.
- 401(k) with company match, life insurance, vacation and sick paid time off accruals, and 11 paid holidays.
- Tuition reimbursement and a work environment that encourages excellence.
- Approximately 10% travel, primarily to academic institutions and also to cyber and physical ranges or industry events.
- Position filling is contingent upon funding.