Ampcus, Inc
AI Engineer/Data Scientist – AI Business Pilots
Chantilly, Virginia
Sponsorship not specifiedDetected 71 days ago
Vector DatabasesAWSAPI DevelopmentMachine LearningData EngineeringData ScienceLLMsRAGAgentic AIAI OrchestrationStatisticsProcess Improvement
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Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- Ensure pilot objectives are fully achieved, which may include:
- Enterprise adoption recommendations and more.
Responsibilities
- The Senior AI Engineer/Data Scientist serves as a technical leader and hands-on practitioner responsible for planning, leading, developing, and delivering AI pilots that drive measurable business value.
- The position combines expertise in AI engineering, agentic AI systems design, data science, and solution delivery to design and deliver pilots (production and proof of concept) that drive value and AI adoption.
- Lead/support end-to-end execution of AI pilots from concept through delivery, adoption, and evaluation.
- Architect and develop Retrieval-Augmented Generation (RAG) systems, agentic workflows, multi-agent architectures, knowledge assistants, decision-support systems, and intelligent automation capabilities.
- Design secure, compliant, resilient, and scalable architectures suitable for enterprise environments.
- Design, develop, test, and deploy agentic AI solutions capable of autonomous reasoning, planning, orchestration, and task execution.
Requirements
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or a related technical discipline.
- 10+ years of experience in data science, software engineering, AI engineering, advanced analytics, or related technical disciplines.
- 5+ years of experience leading complex analytics, AI, and/or technology delivery initiatives.
Nice to have
- Experience deploying pilot and production-grade AI systems in enterprise environments.
- Experience supporting regulated or government-focused cloud environments.
- Familiarity with frontier AI model ecosystems and model evaluation methodologies.
- Knowledge of AI observability, monitoring, governance, and operational frameworks.
- AWS certification(s), AI/ML certification(s), or comparable technical credentials.
- Understand project objectives, milestones, success metrics, and delivery plans.
- Business process improvement
- Technology validation
Skills
- Experiment with emerging AI technologies and frontier models to determine applicability and organizational value.
- Vector Databases and Knowledge Retrieval
- Cloud-Native Application Architecture
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