AIS (Applied Information Sciences)

Full-stack Engineer

• Work in a team with other smart AIS employees using cutting-edge technologies to solve challenging business problems.
• Build elegant, scalable, extensible, cost-effective solutions with an eye toward innovation and agility.
• Utilize problem-solving and decision-making skills to understand client pain points and to self-troubleshoot as challenges arise.
• Collaborate with other development team members and project managers to deliver solutions that surpass client expectations while meeting deadlines and budgets.
• Design, write, unit test, and document application code to successfully deliver project-based work.
• Work in an agile environment with participation in daily stand-ups/scrum.
• Design, write, test, troubleshoot, and document application code
• Provide mentorship to junior Developers.
• Learn new technologies and know industry standards, best practices, and trends.

Full Stack Development Lead

Solutions Engineer

• Lead and architect complex distributed systems, integrating multiple web applications and ensuring seamless collaboration across teams.
• Collaborate closely with stakeholders to drive architectural decisions and implement best practices.
• Act as the interface between solution and development teams, ensuring alignment between strategic goals and technical implementation.
• Provide hands-on support and guidance to development teams, especially in frontend (React) and backend (C#, .Net, Azure) architectures.
• Support and mentor team members, fostering a coachable and highly collaborative environment.

Solution Architect

Data Engineer

• Work in a team with other smart AIS employees and use cutting-edge technologies to solve challenging enterprise problems.
• Design and modernize data platform solutions using Azure data services such as Data Factory, Data Lake, Azure Event Hub, Azure Synapse Analytics, Azure Databricks, Azure Machine Learning, and Azure AI Services.
• Design scalable data processing and analytics solutions, including multi-cloud and on-premises big data storage for various data types and large-scale data processing.
• Apply your skills in Azure Cognitive Services, Azure PaaS, and Machine Learning services.
• Use experience working with Azure Data & Storage, Azure Analytics & Azure IoT tools, and the traditional Microsoft BI Stack.
• Provide mentorship to more junior consultants.

Data Architect

Cloud Engineer

• Design, implement, and maintain secure, scalable, and resilient cloud-based solutions in support of federal defense and intelligence missions.
• Develop, document, and implement cloud reference architectures aligned to DoD and federal cloud strategies.
• Design resilient, scalable, and cost-optimized cloud solutions (IaaS, PaaS, SaaS) across environments.
• Build Infrastructure as Code (IaC) solutions using Terraform, Bicep, or CloudFormation to accelerate deployments.
• Implement CI/CD pipelines to support automated testing, compliance, and deployment.
• Apply DoD Zero Trust principles and STIG compliance to cloud workloads.
• Monitor and optimize cloud performance, cost, and availability.

• Architect Scalable Data Solutions: Design and implement data architectures optimized for machine learning workflows, including data ingestion, transformation, storage, and retrieval.
• ML Infrastructure Design: Collaborate with data scientists and engineers to build infrastructure for training, testing, and deploying ML models at scale.
• Data Governance & Quality: Establish data governance frameworks, ensure data integrity, and implement best practices for data security and compliance.
• Cloud & On-Prem Integration: Develop hybrid data solutions that integrate cloud platforms (e.g., Azure, AWS, GCP) with on-premise systems.
• Model Lifecycle Management: Support MLOps practices including versioning, monitoring, and retraining of models.
• Stakeholder Collaboration: Work closely with business analysts, data scientists, and IT teams to align data architecture with business goals.
• Performance Optimization: Tune data systems for performance, scalability, and cost-efficiency.

Machine Learning Data Architect

Project Manager

• Managing projects, ensuring contractual obligations and margin goals are met while providing an outstanding client experience that will lead to contract renewals and expansions.
• Successfully develop and execute project plans, track deliverables, and document client acceptance of deliverables following AIS' Project Management approach.
• Identify, track, mitigate, and report on risks to delivery excellence, customer satisfaction, and employee satisfaction.
• Maintain situational awareness of day-to-day activities on the ground, serving as the first escalation point for the client.
• Build credibility through effective communication and solutions.
• Communicate with internal stakeholders to support a coordinated response in serving the client.
• Identify and support opportunities for continuous improvement.

Full Stack Development Lead

Solution Architect

Data Architect

Cloud Engineer

Machine Learning Data Architect

Senior Project Manager