Federal AI Engineer: Responsible AI & Automation

Stock Group Analytics

Washington, Northern (District of Columbia, KY)

Hybrid

USD 140,000 - 180,000

Full time

14 days+

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Job summary

Stock Group Analytics in Washington, DC is hiring AI Engineers to design, develop, and deploy AI/ML models and intelligent automation across government use cases. You will guide the full lifecycle from use-case identification and prototyping to production deployment and sustainment, ensuring solutions are ethical, secure, and compliant with federal AI guidelines.

Join a collaborative team applying NLP, OCR, and generative AI to real government challenges.

Qualifications

Bachelor's degree in Computer Science, Data Science, AI, or related field (Master's or Ph.D. preferred).
5+ years of hands-on experience in AI/ML solutions development.
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
Experience with cloud AI platforms (Azure ML, Azure OpenAI, AWS SageMaker).
Knowledge of NLP techniques and tools (spaCy, Hugging Face, NLTK).
Experience with computer vision frameworks and OCR technologies.
Understanding of MLOps practices for model deployment and monitoring.
Familiarity with RPA platforms (UiPath, Power Automate) for intelligent automation.
Strong software development skills.
Understanding of ethical AI and Responsible AI principles.

Responsibilities

Design, develop, and sustain AI/ML models for predictive analytics, computer vision, NLP, and other use cases
Identify and evaluate high-impact AI/ML use cases in collaboration with Government stakeholders
Execute rapid pilot projects in short sprints to demonstrate business value
Develop and deploy intelligent automation workflows integrating AI capabilities with RPA
Build and fine-tune Generative AI solutions including chatbots and copilot assistants
Implement Natural Language Processing (NLP) and Intelligent OCR solutions for unstructured data
Research emerging AI tools and techniques and recommend those that improve efficiency
Ensure AI outputs are interpretable and bias-mitigated in compliance with federal AI guidelines
Embed Responsible AI principles (transparency, fairness, explainability) in all deployments
Support establishment of AI Governance Framework and AI Governance Board
Integrate AI models into production systems with appropriate monitoring and logging
Document AI model architectures, training data, and performance metrics