Note: The job is a remote job and is open to candidates in USA. Joint Technology Solution, Inc. is seeking an experienced Subject Matter Expert in Artificial Intelligence. This role involves providing technical expertise and support for AI initiatives, overseeing project delivery, and collaborating with stakeholders to enhance enterprise AI capabilities.
Responsibilities
- Translates complex operational, scientific, and business challenges and user feedback into actionable technical requirements, system architectures, and implementation plans for AI-driven solutions
- Oversees the lifecycle of AI initiatives from a technical perspective by prioritizing features that maximize organizational value while managing resource constraints and risks
- Coordinates between cross-functional teams to ensure that project milestones and AI solutions align with enterprise architecture and cybersecurity requirements as well as meet both technical benchmarks and user expectations
- Provides expert guidance to development teams on AI/ML architectural design, software engineering practices, data pipelines, model selection, deployment strategies, and the implementation of best practices, including testing and maintenance
- Conducts rigorous technical assessments and architecture reviews of proposed and ongoing AI projects to assess feasibility, scalability, security, performance standards, operational readiness, and risk
- Identifies potential technical bottlenecks and ethical considerations in AI workflows and recommends proactive solutions to timely and responsible delivery
- Recommend and implement best practices for software engineering, Development, Security, and Operations (DevSecOps), Machine Learning Operations (MLOps), testing, monitoring, observability, and lifecycle management of AI systems
- Leads the end-to-end development of AI projects for NWS enterprise priorities that do not have a development team, ensuring they are built for production-grade reliability and scalability
- Implement and support Continuous Integration and Continuous Deployment (CI/CD), DevSecOps, and MLOps practices, including automated testing, model versioning, deployment automation, monitoring, and incident response
- Troubleshoot operational issues, performance bottlenecks, and deployment challenges associated with AI systems and supporting infrastructure
- Supports the development of proof-of-concepts to test emerging AI technologies and their potential application within the enterprise
- Distills sophisticated AI architectures, concepts, engineering challenges, and model performance metrics into clear, non-technical insights for executive leadership and stakeholders
- Prepare technical documentation, implementation plans, architecture diagrams, deployment guidance, and operational support materials
- Facilitates dialogue between developers, data scientists, engineers, and product owners to maintain clarity on project goals and ensure continuous feedback
- Supports the communication of current NWS AI capabilities as well as future AI opportunities, presenting technical progress, operational risks, strategic wins, and possibilities in formal reports and briefings
- Recommend explicitly referencing Development, Security, and Operations (DevSecOps) and Machine Learning Operations (MLOps) practices. These terms communicate that the role is expected to support the operationalization of AI capabilities, including automated deployment pipelines, model versioning, security controls, monitoring, observability, and ongoing maintenance
Skills
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Information Technology, or related field
- Minimum 7-10 years of experience in software engineering, systems architecture, data science, or related technical fields
- Minimum 5 years of experience designing, developing, and implementing AI/ML solutions in production environments
- Experience leading complex technical projects involving cross-functional teams and stakeholders
- Ability to obtain and maintain a Public Trust or other required federal security clearance
- Demonstrated experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar technologies
- Experience developing and deploying machine learning models in cloud environments such as AWS, Azure, or Google Cloud
- Strong knowledge of DevSecOps, MLOps, CI/CD pipelines, and software development lifecycle best practices
- Experience designing scalable system architectures and data pipelines
- Proficiency in Python and other relevant programming languages
- Experience implementing monitoring, testing, version control, and operational support for AI systems
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