Upslope Advisors is seeking an AI Developer/Engineer to design, build, evaluate, deploy, and sustain production artificial intelligence and machine-learning solutions for a federal civilian agency. The successful candidate will support retrieval-augmented generation applications, advanced chatbots, predictive models, document-processing solutions, fraud-detection capabilities, workflow automation, and other AI-enabled mission applications. This position requires hands-on AI and machine-learning engineering, cloud integration, software development, model evaluation, and production deployment experience. Key Responsibilities AI and Machine-Learning Solution Development
- Design, develop, train, evaluate, and optimize artificial intelligence and machine-learning models.
- Build production retrieval-augmented generation applications, advanced chatbots, document-processing services, predictive models, and automation tools.
- Select appropriate models, algorithms, embeddings, vector stores, and retrieval strategies.
- Develop model-performance metrics and document experiments, assumptions, limitations, and results.
- Support the development of fraud-detection, forecasting, public-sector analytics, and other mission-focused AI capabilities.
Model Evaluation and Responsible AI
- Evaluate model accuracy, groundedness, hallucination risk, bias, drift, reliability, security, and overall performance.
- Implement model guardrails, human-in-the-loop controls, logging, and operational monitoring.
- Conduct prompt testing, retrieval-quality testing, hallucination testing, and performance validation.
- Identify model limitations and recommend appropriate technical or operational controls.
- Support continuous evaluation and improvement of deployed AI solutions.
Data Engineering and Application Integration
- Develop Python-based artificial intelligence, data-processing, and API services.
- Perform data preprocessing, feature engineering, statistical analysis, and data-quality validation.
- Work with large and complex datasets from multiple structured and unstructured sources.
- Integrate AI and machine-learning models into full-stack applications and enterprise technology platforms.
- Support front-end, middleware, database, API, and back-end integration.
- Refactor and onboard externally developed AI applications into controlled enterprise environments.
Cloud, Automation, and Production Operations
- Develop infrastructure as code using Terraform or comparable tools.
- Build and maintain Git-based continuous integration and continuous delivery pipelines.
- Implement automated testing, deployment controls, and release-management processes.
- Integrate observability, alerting, performance monitoring, and model-monitoring capabilities.
- Support the deployment and operation of AI applications in secure cloud and enterprise environments.
- Troubleshoot application, model, data, integration, and production-performance issues.
Documentation and Collaboration
- Prepare architecture, build, operating, security, model, and user documentation.
- Document models, experiments, processes, technical decisions, assumptions, and limitations.
- Collaborate with product owners, data scientists, software engineers, cloud engineers, security teams, and customer stakeholders.
- Support demonstrations, technical reviews, knowledge transfer, and user adoption activities.
- Support accessibility, security authorization, data-governance, and continuous-monitoring activities.
Minimum Qualifications
- Bachelor's or master's degree in computer science, artificial intelligence, machine learning, data science, or a related field.
- Five to ten years of relevant software-development, data-science, or AI and machine-learning engineering experience.
- Strong proficiency in Python.
- Demonstrated experience designing and deploying machine-learning solutions.
- Experience integrating AI and machine-learning models into production applications.
- Strong understanding of model evaluation, optimization, testing, and performance measurement.
- Experience working with large and complex datasets.
- Experience with data preprocessing, feature engineering, statistical analysis, and data-quality validation.
- Experience with Git, continuous integration and continuous delivery, automated testing, and infrastructure as code.
- Experience integrating monitoring or observability tools into AI systems.
- Familiarity with deep-learning frameworks and modern AI and machine-learning tools.
- Ability to document models, experiments, processes, and technical decisions.
- Strong analytical, troubleshooting, communication, and collaboration skills.
Preferred Qualifications
- Production experience with Azure AI Foundry, Azure OpenAI, Azure Machine Learning, AWS SageMaker, GCP Vertex AI, or comparable platforms.
- Experience developing retrieval-augmented generation applications, vector-search solutions, and advanced chatbots.
- Experience with embeddings, vector databases, retrieval strategies, and document-ingestion pipelines.
- Experience with prompt engineering, large language model evaluation, guardrails, drift monitoring, and hallucination testing.
- Experience with ServiceNow AI, Microsoft 365 Copilot, Salesforce Einstein, or comparable enterprise AI tools.
- Experience supporting federal cloud environments and FedRAMP requirements.
- Knowledge of National Institute of Standards and Technology controls and federal security-authorization processes.
- Experience working with Controlled Unclassified Information or personally identifiable information.
- Experience developing accessible applications that meet Section 508 requirements.
- Experience supporting disaster-response, grants, fraud-detection, forecasting, or public-sector analytics programs.
- Existing favorable federal background investigation.
Citizenship and Suitability Requirements
- U.S. citizenship is required.
- The selected candidate must be able to obtain and maintain a federal public trust determination.
