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Generative AI Engineer

4 months, 3 weeks ago
Anywhere
Full-time
Senior
AI Engineer
Software Development
Agile AWS Bitbucket Docker Feature Engineering Generative AI Git GitHub GitLab Kubernetes LLM Machine Learning NumPy Pandas Python PyTorch Scikit-learn TensorFlow Terraform
Apply Now
NextGen Federal Systems

NextGen Federal Systems

NextGen Federal Systems is a minority-owned small business that delivers transformative software and IT solutions to the defense and intelligence communities. They provide award-winning IT solutions and services, utilizing a mixed model of mission serv...

Internet Software & Services
51-250
Founded 2011
View All Jobs 2

Description

  • Research, design, develop, and deploy AI/ML and Generative AI solutions for mission-focused use cases.
  • Participate in all phases of the software engineering lifecycle, including requirements analysis, solution design, model development, integration, deployment, evaluation, and testing.
  • Work with a technical team to implement and transition AI/ML and Generative AI capabilities that meet client operational requirements.
  • Develop solutions using Python and AI/ML or data science libraries and frameworks.
  • Design or integrate LLM-based applications such as chat interfaces, document Q&A systems, workflow automation tools, summarization tools, and decision-support systems.
  • Build and manage cloud-based AI/ML solutions using cloud services and cloud-native AI capabilities.
  • Apply machine learning best practices for data preprocessing, feature engineering, model training, evaluation, performance measurement, and deployment.
  • Collaborate in an Agile, fast-paced, distributed development environment.
  • Communicate technical concepts clearly to both technical and non-technical stakeholders.
  • Support evaluation of LLM and RAG systems using qualitative and quantitative methods.

Requirements

  • Bachelor's degree in Computer Science, Math, Engineering, or a related field; Master's or PhD preferred.
  • 6+ years of work-related experience in applied machine learning, data science, software engineering, or AI/ML system development; 4+ years or 2+ years may be acceptable depending on degree level.
  • Experience designing, developing, or deploying machine learning, Generative AI, or data-driven software solutions.
  • Experience developing solutions with Python and libraries such as pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, LangChain, or LlamaIndex.
  • 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 such as 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, evaluation, performance metrics, and deployment best practices.
  • Strong written and verbal communication skills with the ability to explain technical concepts to technical and non-technical stakeholders.
  • Proficiency with Microsoft Office tools, including Word, Excel, PowerPoint, and Outlook.
  • Experience working in an Agile development lifecycle, preferred.
  • Experience with AWS Bedrock and related AWS AI/ML services, preferred.
  • Familiarity with Model Context Protocol and MCP servers, tools, resources, or agent-accessible services, preferred.
  • Experience with RAG architectures, including document ingestion, chunking, embedding models, vector databases, metadata filtering, reranking, and response evaluation, preferred.
  • Experience with agentic AI workflows, tool-calling, function-calling, multi-step reasoning workflows, or orchestration frameworks, preferred.
  • Experience evaluating LLM or RAG systems using human evaluation, LLM-as-judge approaches, RAGAS-style metrics, hallucination analysis, or task-specific performance measures, preferred.
  • Experience deploying AI/ML solutions using AWS, Docker, Kubernetes, CI/CD pipelines, Terraform, or similar DevOps tools, preferred.
  • Experience with Git, GitLab, GitHub, or Bitbucket, preferred.
  • History of academic publications, conference presentations, technical reports, demos, or client-facing briefings, preferred.

Interested in this position?

Apply directly on the company website

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