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Senior AI Engineer/Data Scientist – AI Business Pilots
Ampcus Inc
4 hours ago
Full-time
On-site
Chantilly, Virginia, United States
We’re hiring!
Candidates who are authorized to work in the U.S. without sponsorship are encouraged to apply. Candidates must also be eligible to obtain a Public Trust clearance.
Senior AI Engineer/Data Scientist – AI Business Pilots
POSITION SUMMARY: 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. This role leads and supports end-to-end AI pilot engagements that address strategic business challenges, supports executive and VIP requests, validates emerging AI technologies, accelerates continuous enterprise AI adoption, and establishes pathways to production deployment. 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.
PRIMARY RESPONSIBILITIES: AI Pilot Planning and Delivery • Lead/support end-to-end execution of AI pilots from concept through delivery, adoption, and evaluation. • Understand project objectives, milestones, success metrics, and delivery plans. • Ensure pilot objectives are fully achieved, which may include: o Business process improvement o Technology validation o Proof-of-concept development o Executive demonstrations o Expanded functionality o Production deployment o Enterprise adoption recommendations and more.
AI Solution Architecture and Engineering • Design and implement advanced AI solutions using modern cloud-native architectures. • Build scalable AI applications, intelligent agents, APIs, orchestration frameworks, user interfaces, and supporting components. • Architect and develop Retrieval-Augmented Generation (RAG) systems, agentic workflows, multi-agent architectures, knowledge assistants, decision-support systems, and intelligent automation capabilities. • Integrate AI capabilities with enterprise systems, services, applications, and data ecosystems. • Design secure, compliant, resilient, and scalable architectures suitable for enterprise environments.
Data Science and Advanced Analytics • Apply advanced statistical techniques and optimization approaches to solve complex business problems. • Establish measurable evidence supporting AI investment and adoption decisions. Agentic AI and Generative AI Development • Design, develop, test, and deploy agentic AI solutions capable of autonomous reasoning, planning, orchestration, and task execution. • Develop solutions utilizing large language models (LLMs), foundation models, AI agents, tool-use frameworks, and emerging autonomous AI capabilities. • Implement prompt engineering, context engineering, agent orchestration, memory management, grounding strategies, and evaluation frameworks. • Evaluate and optimize model performance, cost, security, reliability, and business effectiveness. • Experiment with emerging AI technologies and frontier models to determine applicability and organizational value.
AWS AI Platform Development • Design and implement AI solutions utilizing AWS cloud services and enterprise cloud-native patterns. • Leverage Amazon Bedrock and associated foundation and frontier models to support enterprise AI initiatives. • Design and implement secure, scalable, and compliant AI architectures utilizing AWS services and capabilities. • Collaborate with cloud, security, and infrastructure teams to ensure successful pilot enablement, deployment and operationalization.
AI Technology Evaluation and Innovation • Continuously evaluate emerging AI technologies, platforms, frameworks, tooling, and industry advancements. • Assess frontier AI models, agentic AI frameworks, and enterprise AI platforms for organizational applicability. • Conduct technology proofs-of-value and technical evaluations. • Establish recommendations regarding technology selection, architecture patterns, and deployment approaches. • Drive organizational innovation through practical application of emerging AI capabilities.
REQUIRED QUALIFICATIONS: Education • Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or a related technical discipline. • Master's degree preferred. Experience • 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. • Demonstrated experience delivering agentic AI solutions from concept through operational deployment. o Foundation Model Evaluation and Optimization o API Development and System Integration o Vector Databases and Knowledge Retrieval o Data Engineering and ETL Pipelines o Cloud-Native Application Architecture o Security and Responsible AI Principles
PREFERRED QUALIFICATIONS: • Experience building AI agents capable of autonomous planning, tool usage, workflow execution, and decision support. • 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.
Candidates who are authorized to work in the U.S. without sponsorship are encouraged to apply. Candidates must also be eligible to obtain a Public Trust clearance.
Senior AI Engineer/Data Scientist – AI Business Pilots
POSITION SUMMARY: 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. This role leads and supports end-to-end AI pilot engagements that address strategic business challenges, supports executive and VIP requests, validates emerging AI technologies, accelerates continuous enterprise AI adoption, and establishes pathways to production deployment. 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.
PRIMARY RESPONSIBILITIES: AI Pilot Planning and Delivery • Lead/support end-to-end execution of AI pilots from concept through delivery, adoption, and evaluation. • Understand project objectives, milestones, success metrics, and delivery plans. • Ensure pilot objectives are fully achieved, which may include: o Business process improvement o Technology validation o Proof-of-concept development o Executive demonstrations o Expanded functionality o Production deployment o Enterprise adoption recommendations and more.
AI Solution Architecture and Engineering • Design and implement advanced AI solutions using modern cloud-native architectures. • Build scalable AI applications, intelligent agents, APIs, orchestration frameworks, user interfaces, and supporting components. • Architect and develop Retrieval-Augmented Generation (RAG) systems, agentic workflows, multi-agent architectures, knowledge assistants, decision-support systems, and intelligent automation capabilities. • Integrate AI capabilities with enterprise systems, services, applications, and data ecosystems. • Design secure, compliant, resilient, and scalable architectures suitable for enterprise environments.
Data Science and Advanced Analytics • Apply advanced statistical techniques and optimization approaches to solve complex business problems. • Establish measurable evidence supporting AI investment and adoption decisions. Agentic AI and Generative AI Development • Design, develop, test, and deploy agentic AI solutions capable of autonomous reasoning, planning, orchestration, and task execution. • Develop solutions utilizing large language models (LLMs), foundation models, AI agents, tool-use frameworks, and emerging autonomous AI capabilities. • Implement prompt engineering, context engineering, agent orchestration, memory management, grounding strategies, and evaluation frameworks. • Evaluate and optimize model performance, cost, security, reliability, and business effectiveness. • Experiment with emerging AI technologies and frontier models to determine applicability and organizational value.
AWS AI Platform Development • Design and implement AI solutions utilizing AWS cloud services and enterprise cloud-native patterns. • Leverage Amazon Bedrock and associated foundation and frontier models to support enterprise AI initiatives. • Design and implement secure, scalable, and compliant AI architectures utilizing AWS services and capabilities. • Collaborate with cloud, security, and infrastructure teams to ensure successful pilot enablement, deployment and operationalization.
AI Technology Evaluation and Innovation • Continuously evaluate emerging AI technologies, platforms, frameworks, tooling, and industry advancements. • Assess frontier AI models, agentic AI frameworks, and enterprise AI platforms for organizational applicability. • Conduct technology proofs-of-value and technical evaluations. • Establish recommendations regarding technology selection, architecture patterns, and deployment approaches. • Drive organizational innovation through practical application of emerging AI capabilities.
REQUIRED QUALIFICATIONS: Education • Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or a related technical discipline. • Master's degree preferred. Experience • 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. • Demonstrated experience delivering agentic AI solutions from concept through operational deployment. o Foundation Model Evaluation and Optimization o API Development and System Integration o Vector Databases and Knowledge Retrieval o Data Engineering and ETL Pipelines o Cloud-Native Application Architecture o Security and Responsible AI Principles
PREFERRED QUALIFICATIONS: • Experience building AI agents capable of autonomous planning, tool usage, workflow execution, and decision support. • 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.