About the Company
VivSoft develops secure software solutions for federal agencies using emerging and open technologies. Its team of strategists, engineers, designers, and software professionals builds Software Factories based on Department of Defense (DoD) reference designs and NIST frameworks for cloud and DevSecOps. Its solutions include AI/ML applications, data science platforms, blockchain, and microservices for defense, healthcare, and civilian government agencies.
About the Role
VivSoft is seeking a Senior AI Engineer to design, develop, and deploy advanced AI and machine learning solutions within large-scale data environments. The role focuses on distributed machine learning, agentic AI workflows, large language models (LLMs), and enterprise data integration to support predictive analytics, intelligent automation, and data-driven decisions.
You will work closely with data engineers, data scientists, and cloud and platform engineering teams to build scalable AI solutions, improve model performance, and deploy AI/ML capabilities in secure and disconnected environments. The position also involves technical leadership, mentoring junior engineers, evaluating AI systems, and maintaining responsible AI practices. An active U.S. Government Secret security clearance and U.S. citizenship are required.
Key Responsibilities
Design, develop, and deploy distributed machine learning models using Apache Spark, Spark MLlib, and Apache Iceberg, applying statistical validation and classical machine learning techniques.
Build agentic AI workflows using LLMs, tool calling, Model Context Protocol (MCP), retrieval-augmented generation (RAG), and multi-step planning to query and analyze enterprise datasets.
Develop and optimize AI-driven data analysis workflows using Amazon Athena, Trino, and Spark SQL.
Optimize AI-generated SQL queries through partition pruning, file layout awareness, query guardrails, and distributed query performance techniques.
Deploy and maintain self-hosted AI/ML model-serving environments on Kubernetes, including disconnected and restricted-network environments.
Orchestrate automated model training, evaluation, and agent workflows using Apache Airflow, AWS services, and Kubernetes.
Use Amazon S3, EC2, and EMR to support distributed AI/ML processing and deployment.
Evaluate LLM and AI agent outputs using structured test datasets, evaluation rubrics, regression testing, and performance metrics.
Monitor AI/ML systems for model drift, performance degradation, and operational failures.
Integrate AI/ML models into production data pipelines and enterprise environments in collaboration with data, cloud, and platform teams.
Apply responsible AI principles, including bias assessment, model transparency, secure data handling, and risk management.
Provide technical leadership, mentor junior engineers, and contribute to engineering best practices.
Maintain technical documentation, architecture designs, operational runbooks, and implementation guidance.
Communicate model behavior, limitations, and technical risks to technical and nontechnical stakeholders.
Required Qualifications
Active U.S. Government Secret security clearance.
U.S. citizenship.
At least 8 years of professional experience in software engineering, data engineering, or machine learning, including at least 4 years of applied AI/ML experience.
Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field. Additional relevant professional experience may substitute for education on a year-for-year basis.
Demonstrated experience developing and deploying distributed machine learning solutions with Apache Spark, including Spark MLlib, and lakehouse table formats such as Apache Iceberg.
Strong statistical knowledge, including probability, statistical inference, regression analysis, experimental design, and model evaluation.
Experience applying classical machine learning algorithms to large-scale datasets.
Hands-on experience building agentic AI workflows involving tool calling, MCP, and multi-step AI agents.
Experience with LLMs, RAG, and vector databases.
Strong understanding of distributed SQL query engines, including Amazon Athena, Trino, and Spark SQL, with the ability to optimize AI-generated queries.
Experience deploying and serving AI/ML models in self-hosted or disconnected environments.
Strong Python programming skills.
Hands-on experience with Apache Airflow for workflow orchestration.
Experience with AWS services, including S3, EC2, and EMR.
Experience deploying and managing applications or AI/ML workloads using Kubernetes.
Strong analytical, critical-thinking, and problem-solving skills.
Ability to collaborate with multidisciplinary technical teams to deploy and operationalize AI/ML solutions.
Ability to explain AI/ML concepts, model behavior, limitations, and technical risks to nontechnical stakeholders.
Experience providing technical leadership and mentoring junior engineers.
Understanding of responsible AI practices, including bias assessment, model evaluation, and secure data handling.
Preferred Qualifications
Master's degree or Ph.D. in Statistics, Computer Science, or a related quantitative field.
Experience developing MCP servers or working with established AI agent frameworks.
Experience implementing LLM evaluation frameworks, automated testing methodologies, and AI guardrails.
Advanced knowledge of Apache Iceberg architecture and internals.
Experience with AWS Glue Data Catalog.
Experience with Terraform and Infrastructure as Code (IaC).
Previous experience supporting federal or DoD AI/ML programs.
Experience designing and implementing scalable AI/ML solutions in secure, highly regulated environments.
Location and Work Arrangement
Remote, United States.
Full-time position.
Active Secret clearance and U.S. citizenship required.
Salary & Benefits
Comprehensive medical, dental, and vision insurance.
Life insurance.
Paid time off, including flexible/combined PTO, bereavement leave, and 11 company-paid holidays.
401(k) retirement plan with employer match.
Professional development training reimbursement.