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Salary
≈ $130k – $285k per year (Estimated)
Location
Remote (United States)
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on May 8, 2026. NextGen Federal Systems scores A on the Alion truth index.

Overview
Company
Impact
Profile match
NextGen Federal Systems is a government technology contractor headquartered in Morgantown, West Virginia, and founded in 2011. The company delivers software development, modeling and simulation, geospatial analysis, data management, and cybersecurity work to defense, intelligence, space, and electronic warfare customers. It holds federal contract vehicles including a GSA schedule and staffs cleared personnel on programs across the United States.

NextGen Federal Systems, LLC. is seeking a Generative AI/Machine Learning Engineer to research, design, develop, and deploy innovative AI/ML and Generative AI solutions across a variety of mission-focused problem sets. The selected candidate will support the development of advanced AI capabilities using technologies such as large language models, retrieval-augmented generation, Model Context Protocol servers, agentic workflows, and cloud-native AI services including AWS Bedrock.

The selected candidate will be part of a distributed development team operating in a dynamic, agile, fast-paced environment and will participate in all phases of the software engineering lifecycle, including research, requirements analysis, solution design, model development, integration, deployment, evaluation, and testing.

Qualifications:

    • Bachelor's [Master's / PhD] Degree in Computer Science, Math, Engineering, or related field
    • 6+ [4+ / 2+] years of work-related experience with applied machine learning, data science, software engineering, or AI/ML system development
    • Experience designing, developing, or deploying machine learning, Generative AI, or data-driven software solutions

Required Skills and Experience:

    · Work as part of a technical team to design, develop, implement, and transition AI/ML and Generative AI capabilities that meet client operational requirements

    · Experience developing solutions using Python and common AI/ML or data science libraries such as pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, LangChain, LlamaIndex, or similar frameworks

    · Familiarity with large language models and Generative AI concepts, including prompt engineering, embeddings, vector databases, retrieval-augmented generation, model evaluation, and responsible AI considerations

    · Experience designing or integrating LLM-based applications, including chat-based interfaces, document question-answering systems, workflow automation, summarization tools, or AI-enabled decision-support systems

    · Experience using cloud services to build, deploy, or manage AI/ML solutions

    · Understanding of machine learning concepts, including data preprocessing, feature engineering, model training, model evaluation, performance metrics, and model deployment best practices

    · Strong written and verbal communication skills, including the ability to explain technical concepts to both technical and non-technical stakeholders

    · Proficiency with Microsoft Office tools, including Word, Excel, PowerPoint, and Outlook

    *Secret Clearance

Desired Skills and Experience:

    • Experience working in an Agile development lifecycle
    • Experience developing Generative AI solutions using AWS Bedrock, including foundation models, Knowledge Bases, Agents, Guardrails, or related AWS AI/ML services
    • Familiarity with Model Context Protocol and the development or integration of MCP servers, tools, resources, or agent-accessible services
    • Experience with retrieval-augmented generation architectures, including document ingestion, chunking strategies, embedding models, vector databases, metadata filtering, reranking, and response evaluation
    • Experience with agentic AI workflows, tool-calling, function-calling, multi-step reasoning workflows, or orchestration frameworks
    • Experience evaluating LLM or RAG-based systems using qualitative and quantitative methods, such as human evaluation, automated LLM-as-judge approaches, RAGAS-style metrics, hallucination analysis, or task-specific performance measures
    • Experience deploying AI/ML solutions using cloud-native and DevOps technologies such as AWS, Docker, Kubernetes, CI/CD pipelines, Terraform, or similar tools
    • Experience with configuration management and version control technologies such as Git, GitLab, GitHub, or Bitbucket
    • History of academic publications, conference presentations, technical reports, demos, or client-facing briefings
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