Senior AI Engineer
VivSoft Technologies · Remote
About this role
## Senior AI Engineer **Location:** Remote (USA) **Employment Type:** Full-Time **Clearance Required:** Active Secret Clearance --- ### About VivSoft VivSoft is a diverse team building secure software solutions for complex federal problems using emerging and open technologies. They develop **secure Software Factories** grounded in **DoD reference designs** and **NIST frameworks** for **Cloud** and **DevSecOps**, delivering **AI/ML applications**, **data platforms**, **blockchain**, and **microservices** for DoD, healthcare, and civilian agencies. --- ### Job Summary Design, develop, and deploy advanced **AI/ML solutions** for complex, large-scale data environments. You’ll build **distributed machine learning models**, develop **agentic AI workflows**, and integrate **LLMs** with enterprise data platforms to enable **predictive analytics**, **intelligent automation**, and **data-driven decision-making**—including in **secure and disconnected environments**. --- ### Key Responsibilities - Design, develop, and deploy **distributed ML models** using **Apache Spark** and **Apache Iceberg**. - Build **agentic AI workflows** with **LLMs**, **tool calling**, **Model Context Protocol (MCP)**, **RAG**, and **multi-step planning**. - Develop and optimize AI-driven data analysis workflows using **Amazon Athena**, **Trino**, and **Spark SQL**. - Optimize agent-generated SQL (e.g., **partition pruning**, **file layout awareness**, **query guardrails**, distributed performance techniques). - Deploy and maintain **self-hosted AI/ML model-serving** on **Kubernetes**, including **disconnected/restricted networks**. - Orchestrate automated **model training/evaluation** and agent workflows using **Apache Airflow**, **AWS**, and **Kubernetes**. - Leverage **AWS** (S3, EC2, EMR) for distributed AI/ML processing and deployment. - Evaluate LLM/agent outputs using structured datasets, rubrics, regression testing, and performance metrics. - Monitor for **model drift**, performance degradation, and operational failure modes. - Integrate AI/ML into production data pipelines and enterprise environments. - Apply **responsible AI** practices (bias assessment, transparency, secure data handling, risk management). - Provide technical leadership, mentor engineers, and contribute to best practices. - Maintain technical documentation, architecture designs, and operational runbooks. - Communicate model behavior, limitations, and technical risks to technical and nontechnical stakeholders. --- ### Required Qualifications & Skills - **Active U.S. Government Secret clearance** and **U.S. citizenship required**. - **8+ years** professional experience in software engineering, data engineering, or ML (**4+ years** applied AI/ML). - Bachelor’s degree in a related technical field (or equivalent experience). - Strong experience with **distributed ML** using **Apache Spark** (including **Spark MLlib**) and **lakehouse** formats like **Apache Iceberg**. - Strong statistical foundation (probability, inference, regression, experimental design, model evaluation). - Experience with **classical ML** on large-scale datasets. - Hands-on agentic AI workflow experience (tool calling, **MCP**, multi-step agents). - Experience with **LLMs**, **RAG**, and **vector databases**. - Strong understanding of distributed SQL engines (**Athena**, **Trino**, **Spark SQL**) and query optimization. - Experience deploying/serving AI/ML in **self-hosted/disconnected** environments. - Strong **Pyt
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